ECTA 2019 Abstracts


Full Papers
Paper Nr: 5
Title:

Distributed Multi-objective Particle Swarm Optimization using Time-delayed Virtual Global Best Method

Authors:

Yuji Sato, Shota Ueno and Toshio Hirotsu

Abstract: To reduce the computational cost of particle swarm optimization (PSO) methods, research has begun on the use of Graphics Processing Units (GPUs) to achieve faster processing speeds. However, since PSO methods search based on a global best value, they are hampered by the frequent need for communication with global memory. Even using a standard PSO that uses a local best value does not solve this problem. In this paper, we propose a virtual global best method that speeds up computations by defining a time-delayed global best as a virtual global best in order to reduce the frequency of communication with low-speed global memory. We also propose a method that combines decomposition-based multi-objective PSO (MOPSO/D) with a virtual global best method to speed up multi-objective particle swarm optimization by running it in parallel while maintaining search accuracy, and we demonstrate the effectiveness of this approach by using a number of unimodal/multimodal single objective benchmark test functions and three classical benchmark test functions with two objectives.

Paper Nr: 8
Title:

Evolutionary Techniques in Lattice Sieving Algorithms

Authors:

Thijs Laarhoven

Abstract: Lattice-based cryptography has recently emerged as a prominent candidate for secure communication in the quantum age. Its security relies on the hardness of certain lattice problems, and the inability of known lattice algorithms, such as lattice sieving, to solve these problems efficiently. In this paper we investigate the similarities between lattice sieving and evolutionary algorithms, how various improvements to lattice sieving can be viewed as applications of known techniques from evolutionary computation, and how other evolutionary techniques can benefit lattice sieving in practice.

Paper Nr: 10
Title:

A Regression-like Classification System for Geometric Semantic Genetic Programming

Authors:

Illya Bakurov, Mauro Castelli, Francesco Fontanella and Leonardo Vanneschi

Abstract: Geometric Semantic Genetic Programming (GSGP) is a recent variant of Genetic Programming, that is gaining popularity thanks to its ability to induce a unimodal error surface for any supervised learning problem. Nevertheless, so far GSGP has been applied to the real world basically only on regression problems. This paper represents an attempt to apply GSGP to real world classification problems. Taking inspiration from Perceptron neural networks, we represent class labels as numbers and we use an activation function to constraint the output of the solutions in a given range of possible values. In this way, the classification problem is turned into a regression one, and traditional GSGP can be used. In this work, we focus on binary classification; logistic constraining outputs in [0,1] is used as an activation function and the class labels are transformed into 0 and 1. The use of the logistic activation function helps to improve the generalization ability of the system. The presented results are encouraging: our regression-based classification system was able to obtain results that are better than, or comparable to, the ones of a set of competitor machine learning methods, on a rather rich set of real-life test problems.

Paper Nr: 12
Title:

A Winning Score-based Evolutionary Process for Multi-and Many-objective Peptide Optimization

Authors:

Susanne Rosenthal and Markus Borschbach

Abstract: Target identification as part of drug design is a long process with high laboratory evaluation costs since optimal candidate leads have to be identified in an iterative process including the determination of diverse physiochemical properties, which have to be optimized simultaneously. MOEAs have become an established optimization method in in silico-aided drug design processes. Since target identification becomes more complex, the dimension of molecular optimization problems increases. Less work has been done so far to evolve an evolutionary process efficiently solving both, multi- and many-objective molecular optimization problems while considering application-specific conditions of molecule optimization. This work presents the enhancement of a MOEA especially evolved for molecular optimization. The proposed algorithm is applicable to multi- and many-objective molecular optimization problems identifying a selected number of qualified candidate peptides within a very low number of iterations. It has a simple framework structure and optionally uses two types of winning-score ranking method as survival selection. Default parameters are provided in the components to enable a non-expert use. This algorithm is benchmarked to the recently proposed and promising AnD (ANgle-based selection and shift-based Density estimation strategy) on molecular optimization problems up to 6 objectives. Furthermore, the selection principles are exemplarily compared and discussed.

Paper Nr: 13
Title:

Using Population-based Metaheuristics and Trend Representative Testing to Compose Strategies for Market Timing

Authors:

Ismail Mohamed and Fernando B. Otero

Abstract: Market Timing is the capacity of deciding when to buy or sell a given asset on a financial market. Market Timing strategies are usually composed of components that process market context and return a recommendation whether to buy or sell. The main issues with composing market timing strategies are twofold: (i) selecting the signal generating components; and (ii) tuning their parameters. In previous work, researchers usually attempt to either tune the parameters of a set of components or select amongst a number of components with predetermined parameter values. In this paper, we approach market timing as one integrated problem and propose to solve it with two variants of Particle Swarm Optimization (PSO). We compare the performance of PSO against a Genetic Algorithm (GA), the most widely used metaheuristic in the domain of market timing. We also propose the use of trend representative testing to circumvent the issue of overfitting commonly associated with step-forward testing. Results show PSO to be competitive with GA, and that trend representative testing is an effective method of exposing strategies to various market conditions during training and testing.

Paper Nr: 14
Title:

Hybrid Kriging-assisted Level Set Method for Structural Topology Optimization

Authors:

Elena Raponi, Mariusz Bujny, Markus Olhofer, Simonetta Boria and Fabian Duddeck

Abstract: This work presents a hybrid optimization approach that couples Efficient Global Optimization (EGO) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Topology Optimization (TO) of mechanical structures. Both of these methods are regarded as good optimization strategies for continuous global optimization of expensive and multimodal problems, e.g. associated with vehicle crashworthiness. CMA-ES is flexible and robust to changing circumstances. Moreover, by taking advantage of a low-dimensional parametrization introduced by the Evolutionary Level Set Method (EA-LSM) for structural Topology Optimization, such Evolution Strategy allows for dealing with costly problems even more efficiently. However, it is characterized by high computational costs, which can be mitigated by using the EGO algorithm at the early stages of the optimization process. By means of surrogate models, EGO allows for the construction of cheap-to-evaluate approximations of the objective functions, leading to an initial fast convergence towards the optimum in opposition to a poor exploitive behavior. The approach presented here – the Hybrid Kriging-assisted Level Set Method (HKG-LSM) – first uses the Kriging-based method for Level Set Topology Optimization (KG-LSM) to converge fast at the beginning of the optimization process and explore the design space to find promising regions. Afterwards, the algorithm switches to the EA-LSM using CMA-ES, whose parameters are initialized based on the previous model. A static benchmark test case is used to assess the proposed methodology in terms of convergence speed. The obtained results show that the HKG-LSM represents a valuable option for speeding up the optimization process in real-world applications with limited computational resources. As such, the proposed methodology exhibits a much more general potential, e.g. when dealing with high-fidelity crash simulations.

Paper Nr: 16
Title:

Two New Mutation Techniques for Cartesian Genetic Programming

Authors:

Roman Kalkreuth

Abstract: Cartesian Genetic Programming is often used with a point mutation as the sole genetic operator. In this paper, we propose two phenotypic mutation techniques and take a step towards advanced phenotypic mutations in Cartesian Genetic Programming. The functionality of the proposed mutations is inspired by biological evolution which mutates DNA sequences by inserting and deleting nucleotides. Experiments with boolean functions problem show a better search performance when the proposed mutations are used. The results of our experiments indicate that the proposed mutations are beneficial for the use of Cartesian Genetic Programming.

Paper Nr: 20
Title:

Generalized Lehmer Mean for Success History based Adaptive Differential Evolution

Authors:

Vladimir Stanovov, Shakhnaz Akhmedova, Eugene Semenkin and Mariia Semenkina

Abstract: The Differential Evolution (DE) is a highly competitive numerical optimization algorithm, with a small number of control parameters. However, it is highly sensitive to the setting of these parameters, which inspired many researchers to develop adaptation strategies. One of them is the popular Success-History based Adaptation (SHA) mechanism, which significantly improves the DE performance. In this study, the focus is on the choice of the metaparameters of the SHA, namely the settings of the Lehmer mean coefficients for scaling factor and crossover rate memory cells update. The experiments are performed on the LSHADE algorithm and the Congress on Evolutionary Computation competition on numerical optimization functions set. The results demonstrate that for larger dimensions the SHA mechanism with modified Lehmer mean allows a significant improvement of the algorithm efficiency. The theoretical considerations of the generalized Lehmer mean could be also applied to other adaptive mechanisms.

Paper Nr: 23
Title:

Fireworks Algorithm versus Plant Propagation Algorithm

Authors:

Wouter Vrielink and Daan van den Berg

Abstract: In recent years, the field of Evolutionary Algorithms has seen a tremendous increase in novel methods. While these algorithmic innovations often show excellent results on relatively limited domains, they are less often rigorously cross-tested or compared to other state-of-the-art developments. Two of these methods, quite similar in their appearance, are the Fireworks Algorithm and Plant Propagation Algorithm. This study compares the similarities and differences between these two algorithms, from both quantitative and qualitative perspectives, by comparing them on a set of carefully chosen benchmark functions. The Fireworks Algorithm outperforms the Plant Propagation Algorithm on the majority of these, but when the functions are shifted slightly, Plant Propagation gives better results. Reasons behind these surprising differences are presented, and comparison methods for evolutionary algorithms are discussed in a wider context. All source code, graphs, test functions, and algorithmic implementations have been made publicly available for reference and further reuse.

Short Papers
Paper Nr: 3
Title:

New Designs of k-means Clustering and Crossover Operator for Solving Traveling Salesman Problems using Evolutionary Algorithms

Authors:

Ismail M. Ali, Daryl Essam and Kathryn Kasmarik

Abstract: The traveling salesman problem is a well-known combinatorial optimization problem with permutation-based variables, which has been proven to be an NP-complete problem. Over the last few decades, many evolutionary algorithms have been developed for solving it. In this study, a new design that uses the k-means clustering method, is proposed to be used as a repairing method for the individuals in the initial population. In addition, a new crossover operator is introduced to improve the evolving process of an evolutionary algorithm and hence its performance. To investigate the performance of the proposed mechanism, two popular evolutionary algorithms (genetic algorithm and differential evolution) have been implemented for solving 18 instances of traveling salesman problems and the results have been compared with those obtained from standard versions of GA and DE, and 3 other state-of-the-art algorithms. Results show that the proposed components can significantly improve the performance of EAs while solving TSPs with small, medium and large-sized problems.

Paper Nr: 6
Title:

Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling

Authors:

Yahia Zakaria, Ahmed BahaaElDin and Mayada Hadhoud

Abstract: Dynamic flexible job shop scheduling is an optimization problem concerned with job assignment in dynamic production environments where future job arrivals are unknown. Job scheduling systems employ a pair of rules: a routing rule which assigns a machine to process an operation and a sequencing rule which determines the order of operation processing. Since hand-crafted rules can be time and effort-consuming, many papers employ genetic programming to generate optimum rule trees from a set of terminals and operators. Since the terminal set can be large, the search space can be huge and inefficient to explore. Feature selection techniques can reduce the terminal set size without discarding important information and they have shown to be effective for improving rule generation for dynamic job shop scheduling. In this paper, we extend a niching-based feature selection technique to fit the requirements of dynamic flexible job shop scheduling. The results show that our method can generate rules that achieves significantly better performance compared to ones generated from the full feature set.

Paper Nr: 7
Title:

A Decomposition-based Approach for Constrained Large-Scale Global Optimization

Authors:

Evgenii Sopov and Alexey Vakhnin

Abstract: Many real-world global optimization problems are too complex for comprehensive analysis and are viewed as “black-box” (BB) optimization problems. Modern BB optimization has to deal with growing dimensionality. Large-scale global optimization (LSGO) is known as a hard problem for many optimization techniques. Nevertheless, many efficient approaches have been proposed for solving LSGO problems. At the same time, LSGO does not take into account such features of real-world optimization problems as constraints. The majority of state-of-the-art techniques for LSGO are based on problem decomposition and use evolutionary algorithms as the core optimizer. In this study, we have investigated the performance of a novel decomposition-based approach for constrained LSGO (cLSGO), which combines cooperative coevolution of SHADE algorithms with the ε-constraint handling technique for differential evolution. We have introduced some benchmark problems for cLSGO, based on scalable separable and non-separable problems from IEEE CEC 2017 benchmark for constrained real parameter optimization. We have tested SHADE with the penalty approach, regular ε-SHADE and ε-SHADE with problem decomposition. The results of numerical experiments are presented and discussed.

Paper Nr: 21
Title:

A Multiobjective Artificial Bee Colony Algorithm based on Decomposition

Authors:

Guang Peng, Zhihao Shang and Katinka Wolter

Abstract: This paper presents a multiobjective artificial bee colony (ABC) algorithm using the decomposition approach for improving the performance of MOEA/D (multiobjective evolutionary algorithm based on decomposition). Using a novel reproduction operator inspired by ABC, we propose MOEA/D-ABC, a new version of MOEA/D. Then, a modified Tchebycheff approach is adopted to achieve higher diversity of the solutions. Further, an adaptive normalization operator can be incorporated into MOEA/D-ABC to solve the differently scaled problems. The proposed MOEA/D-ABC is compared to several state-of-the-art algorithms on two well-known test suites. The experimental results show that MOEA/D-ABC exhibits better convergence and diversity than other MOEA/D algorithms on most instances.

Paper Nr: 24
Title:

Playing Iterated Rock-Paper-Scissors with an Evolutionary Algorithm

Authors:

Rémi Bédard-Couture and Nawwaf Kharma

Abstract: Evolutionary algorithms are capable offline optimizers, but are usually left out as a good option for a game-playing artificial intelligence. This study tests a genetic algorithm specifically developed to compete in a Rock-Paper-Scissors competition against the latest opponent from each type of algorithm. The challenge is big since the other players have already seen multiple revisions and are now at the top of the leaderboard. Even though the presented algorithm was not able to take the crown (it came second), the results are encouraging enough to think that against a bigger pool of opponents of varying difficulty it would be in the top tier of players since it was compared only to the best. This is no small feat since this is an example of how a carefully designed evolutionary algorithm can act as a rapid adaptive learner, rather than a slow offline optimizer.

Paper Nr: 26
Title:

Modified Differential Evolution in the Load Balancing Problem for the iFDAQ of the COMPASS Experiment at CERN

Authors:

Ondřej Šubrt, Martin Bodlák, Matouš Jandek, Vladimír Jarý, Antonín Květoň, Josef Nový, Jan Tomsa and Miroslav Virius

Abstract: In general, state-of-the-art data acquisition systems in high energy physics experiments must satisfy high requirements in terms of reliability, efficiency and data rate capability. The paper introduces the Load Balancing (LB) problem of the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN and proposes a solution based on genetic algorithms. Since the LB problem is N P-complete, it challenges analytical and heuristic methods in finding optimal solutions in reasonable time. Differential Evolution (DE) is a type of evolutionary algorithms, which has been used in many optimization problems due to its simplicity and efficiency. Therefore, the Modified Differential Evolution (MDE) is inspired by DE and is presented in more detail. The MDE algorithm has newly-designed crossover and mutation operator and its selection mechanism is inspired by Simulated Annealing (SA). Moreover, the proposal uses an adaptive scaling factor and recombination rate affecting the exploration and exploitation of the MDE algorithm. Thus, the MDE represents a new efficient stochastic search technique for the LB problem. The proposed MDE algorithm is examined on two LB test cases and compared with other LB solution methods.

Paper Nr: 27
Title:

Hybrid Cuckoo Search and Harmony Search Algorithm and Its Modifications for the Calibration of Groundwater Flow Models

Authors:

D. K. Valetov, G. D. Neuvazhaev, V. S. Svitelman and E. A. Saveleva

Abstract: Due to inherent uncertainties associated with the groundwater system characterization, the model calibration is the significant step for the obtaining of the reliable predictions that could be used in long-term safety assessment. The following paper is focused on the modelling of the groundwater flow in heterogeneous media using the data of the geological engineering survey for the prospective site of the radioactive waste deep geological disposal at Nizhnekansky granite-gneiss crystalline rock massif (Krasnoyarsk Territory). This case study illustrates the efficiency of heuristic optimization methods and hybridization approach for the calibration of groundwater models.

Paper Nr: 29
Title:

An Intelligent Design Explorer for New Violin Shapes

Authors:

Hao Wang and Lutz Hamel

Abstract: This work focuses on a design tool, the violin design explorer, for classic violin outline design. Our design tool employs an evolution strategies algorithm and can be viewed as a creative evolutionary system. With this system, users without any design knowledge can create their favorite violin outline through a set of simple choices. The underlying violin designs are implemented using the Digital Amati geometry engine. In order to validate the design tool we conducted an anonymous experiment. Four groups of participants were asked to design their favorite violin outline. Design patterns that emerged within these four participant groups during the experiment seem to validate that our design tool works as we envisioned. We view this implementation of the designer as the first stage of a comprehensive violin design tool.

Paper Nr: 31
Title:

Speeding Up Evaluation of Structures for the Angry Birds Game

Authors:

Laura Calle, Juan-Julián Merelo-Guervós, Mario García-Valdez and Antonio Mora-García

Abstract: In this work, we present an original method based on evolutionary algorithms for generating basic structures for the physics-based game Angry Birds, with the ultimate objective of creating Angry Birds levels with the minimum number of constraints. We set out to evolve free-form structures, and this means searching in a larger space. In this paper, we test how using a physics engine enables us to evaluate much more levels than a game engine simulation. In order to do this, we compare the results of experiments using both types of simulators and propose fitness functions accordingly. Results show the execution time drastically drops from 5 hours to less than 20 minutes on average.

Paper Nr: 40
Title:

Tuning Parameters of Differential Evolution: Self-adaptation, Parallel Islands, or Co-operation

Authors:

Christina Brester and Ivan Ryzhikov

Abstract: In this paper, we raise a question of tuning parameters of Evolutionary Algorithms (EAs) and consider three alternative approaches to tackle this problem. Since many different self-adaptive EAs have been proposed so far, it has led to another problem of choice. Self-adaptive modifications usually demonstrate different effectiveness on the set of test functions, therefore, an arbitrary choice of it may result in the poor performance. Moreover, self-adaptive EAs often have some other tuned parameters such as thresholds to switch between different types of genetic operators. On the other hand, nowadays, computing power allows testing several EAs with different settings in parallel. In this study, we show that running parallel islands of a conventional Differential Evolution (DE) algorithm with different CR and F enables us to find its variants that outperform advanced self-adaptive DEs. Finally, introducing interactions among parallel islands, i.e. exchanging the best solutions, helps to gain the higher performance, compared to the best DE island working in an isolated way. Thus, when it is hard to choose one particular self-adaptive algorithm from all existing modifications proposed so far, the co-operation of conventional EAs might be a good alternative to advanced self-adaptive EAs.

Paper Nr: 1
Title:

Universal Learning Machine with Genetic Programming

Authors:

Alessandro Re, Leonardo Vanneschi and Mauro Castelli

Abstract: This paper presents a proof of concept. It shows that Genetic Programming (GP) can be used as a “universal” machine learning method, that integrates several different algorithms, improving their accuracy. The system we propose, called Universal Genetic Programming (UGP) works by defining an initial population of programs, that contains the models produced by several different machine learning algorithms. The use of elitism allows UGP to return as a final solution the best initial model, in case it is not able to evolve a better one. The use of genetic operators driven by semantic awareness is likely to improve the initial models, by combining and mutating them. On three complex real-life problems, we present experimental evidence that UGP is actually able to improve the models produced by all the studied machine learning algorithms in isolation.

Paper Nr: 4
Title:

A Comparative Study of Evolutionary Methods for Feature Selection in Sentiment Analysis

Authors:

Shikhar Garg and Sukriti Verma

Abstract: With the recent surge of social media and other forums, availability of a large volume of data has rendered sentiment analysis an important area of research. Though current state-of-the-art systems have been demonstrated impressive performance, there is still no consensus on the optimum feature selection algorithm for the task of sentiment analysis. Feature selection is an indispensable part of the pipeline in natural language models as the data in this domain has extremely high dimensionality. In this work, we investigate the performance of two meta-heuristic feature selection algorithms namely Binary Bat and Binary Grey Wolf. We compare the results obtained to employing Genetic Algorithm for the same task. We report the results of our experiments on publicly available datasets drawn from two different domains, viz. tweets and movie reviews. We have used SVM, k-NN and Random Forest as the classification algorithms.

Paper Nr: 9
Title:

Enhanced Particle Swarm Optimisation and Multi Objective Optimization for the Orchestration of Edge Cloud Clusters

Authors:

Hafiz F. Shahid and Claus Pahl

Abstract: Load balancing and workload distribution cause challenges for the management of IoT and distributed systems in the edge computing environment. Swarm intelligence is a technology suitable for the management of distributed systems, networks, communication and routing protocols. Swarm intelligence-based PSO algorithms (particle swarm optimization) can be applied for load balancing and task scheduling in cloud computing environments operating through a broker agent. In distributed cloud environments, data is collected and then processed at the center of the cloud, rather than making decision at edge nodes closer to IoT infrastructures. Here, we develop an automated orchestration technique for clustered cloud architectures. An Autonomous Particle Swarm Optimization, called the A-PSO algorithm, is implemented that enables an edge node, such as a remote storage, to work as part of a decentralized, self-adaptive intelligent task scheduling and load balancing agant between resources in distributed systems. Using Multi Objective Optimization (MOO), complementing the A-PSO algorithm, we also include metrics such as Actual Round-Trip Time (ARTT) of tasks assignments to the remote storage to reduce the execution cost. Our A-PSO algorithm can orchestrate the distribution of large volumes of data to remote storage and back in cluster, i.e., coordinated distributed cloud environments.

Paper Nr: 11
Title:

Online Encoder-decoder Anomaly Detection using Encoder-decoder Architecture with Novel Self-configuring Neural Networks & Pure Linear Genetic Programming for Embedded Systems

Authors:

Gabrielė Kasparavičiūtė, Malin Thelin, Peter Nordin, Per Söderstam, Christian Magnusson and Mattias Almljung

Abstract: Recent anomaly detection techniques focus on the use of neural networks and an encoder-decoder architecture. However, these techniques lead to trade offs if implemented in an embedded environment such as high heat management, power consumption and hardware costs. This paper presents two related new methods for anomaly detection within data sets gathered from an autonomous mini-vehicle with a CAN bus. The first method which to the best of our knowledge is the first use of encoder-decoder architecture for anomaly detection using linear genetic programming (LGP). Second method uses self-configuring neural network that is created using evolutionary algorithm paradigm learning both architecture and weights suitable for embedded systems. Both approaches have the following advantages: it is inexpensive regarding resource use, can be run on almost any embedded board due to linear register machine advantages in computation. The proposed methods are also faster by at least one order of magnitude, and it includes both inference and complete training.

Paper Nr: 17
Title:

On the Time Complexity of Simple Cartesian Genetic Programming

Authors:

Roman Kalkreuth and Andre Droschinsky

Abstract: Since its introduction, Cartesian Genetic Programming has been mostly analyzed on an experimental level with boolean function problems. Consequently, there is still little theoretical understanding of Cartesian Genetic Programming. In this paper, we present a first time complexity analysis of Cartesian Genetic Programming. We introduce and analyze a simple mathematical problem and a simple logical boolean problem called SUM and AND. The results of our analysis show that simple CGP is able to solve SUM efficiently in time Θ(nlogn). However, our analysis of the AND problem shows that simple CGP is not able to solve AND efficiently.

Paper Nr: 18
Title:

Genetic Algorithm with Success History based Parameter Adaptation

Authors:

Vladimir Stanovov, Shakhnaz Akhmedova and Eugene Semenkin

Abstract: Genetic algorithm is a popular optimization method for solving binary optimization problems. However, its efficiency highly depends on the parameters of the algorithm. In this study the success history adaptation (SHA) mechanism is applied to genetic algorithm to improve its performance. The SHA method was originally proposed for another class of evolutionary algorithms, namely differential evolution (DE). The application of DE’s adaptation mechanisms for genetic algorithm allowed significant improvement of GA performance when solving different types of problems including binary optimization problems and continuous optimization problems. For comparison, in this study, a self-configured genetic algorithm is implemented, in which the adaptive mechanisms for probabilities of choosing one of three selection, three crossover and three mutation types are implemented. The comparison was performed on the set of functions, presented at the Congress on Evolutionary Computation for numerical optimization in 2017. The results demonstrate that the developed SHAGA algorithm outperforms the self-configuring GA on binary problems and the continuous version of SHAGA is competetive against other methods, which proves the importance of the presented modification.

Paper Nr: 22
Title:

ME2: A Scalable Modular Meta-heuristic for Multi-modal Multi-dimension Optimization

Authors:

Mohiul Islam, Nawwaf Kharma, Vaibhav Sultan, Xiaojing Yang, Mohamed Mohamed and Kalpesh Sultan

Abstract: Map, Explore & Exploit (ME2) is a scalable meta-heuristic for problems in the field of multi-modal, multi-dimension optimization. It has a modular design with three phases, as reflected by its name. Its first phase (Map) generates a set of samples that is mostly uniformly distributed over the search space. The second phase (Explore) explores the neighbourhood of each sample point using an evolutionary strategy, to find a good - not necessarily optimal - set of neighbours. The third phase (Exploit) optimizes the results of the second phase. This final phase applies a simple gradient descent algorithm to find the local optima for each and all of the neighbourhoods, with the objective of finding a/the global optima of the whole space. The performance of ME2 is compared, on a fair basis, with the performance of benchmark optimization algorithms: Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Covariance Matrix Adaptation Evolution Strategy. In most test cases it finds the global optima earlier than the other algorithms. It also scales-up, without loss of performance, to higher dimensions. Due to the distributed nature of ME2’s second and third phase, it can be comprehensively parallelized. The search & optimization process during these two phases can be applied to each sample point independently of all the others. A multi-threaded version of ME2 was written and compared to its single-threaded version, resulting in a near-linear speed-up as a function of the number of cores employed.

Paper Nr: 32
Title:

Intellectual Execution Scheme of Iterative Computational Models based on Symbiotic Interaction with Application for Urban Mobility Modelling

Authors:

Mikhail Melnik, Denis Nasonov and Alexey Liniov

Abstract: In the modern world, with the growth of the volume of processed data arrays, the logic of solving problems also becomes more complex. This leads more and more often to the need to use high-performance computational clusters, such as supercomputers. Created multi-agent simulation applications require not only significant resources but often perform time-consuming complex scenarios, which significantly affects the efficiency of the executed process. However, there are various mechanisms for optimizing application execution for different needs. Unfortunately, the specificity of multi-agent simulation does not allow the use of traditional and modern algorithms due to the iteratively variable workload and limitations of a system software installed on the supercomputers. In this paper, we propose a four-level scheme for organizing the symbiotic execution (co-design) of multi-agent applications on supercomputers, as well as an effective two-level algorithm for optimizing the flow of the execution of an urban mobility simulation application. The algorithm is based on evolutionary approach and machine learning techniques.

Paper Nr: 39
Title:

Restart Operator for Optimization Heuristics in Solving Linear Dynamical System Parameter Identification Problem

Authors:

Ivan Ryzhikov and Christina Brester

Abstract: In this study, the parameter identification problem for linear dynamical systems is considered. The system is assumed to be represented as a linear differential equation in general form, so the right-hand side equation contains input function and its derivatives. This problem statement extends the order reduction problem, where we need to find the equation of the lower order to approximate the real system output observations. Considered problem is reduced to an optimization one. The reduced problem is complex, and we propose the combination of stochastic optimization algorithm and restart operator. This operator aim is to prevent the algorithm stagnation by starting the search over again if no remarkable solution improvement is detected or if algorithm searches in the area where stagnation had been detected.