An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments

Krämer, Oliver; Gloger, Bartek; Goebels, Andreas

doi:10.1145/1276958.1277099

Cited by 16 publications

(8 citation statements)

References 5 publications

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“…A similar situation can be found in the machine learning community (Demšar 2006). • The experimental design consists of a set of techniques which comprise methodologies for adjusting the parameters of the algorithms depending on the settings used and results obtained (Bartz-Beielstein 2006;Kramer 2007). In our study, we are not interested in this topic; we assume that the algorithms in a comparison have obtained the best possible results, depending on an optimal adjustment of their parameters in each problem.…”

Section: Introductionmentioning

confidence: 99%

A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

et al. 2008

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In recent years, there has been a growing interest for the experimental analysis in the field of evolutionary algorithms. It is noticeable due to the existence of numerous papers which analyze and propose different types of problems, such as the basis for experimental comparisons of algorithms, proposals of different methodologies in comparison or proposals of use of different statistical techniques in algorithms' comparison.In this paper, we focus our study on the use of statistical techniques in the analysis of evolutionary algorithms' behaviour over optimization problems. A study about the required conditions for statistical analysis of the results is presented by using some models of evolutionary algorithms for real-coding optimization. This study is conducted in two ways: single-problem analysis and multiple-problem analysis. The results obtained state that a parametric statistical analysis could not be appropriate specially when we deal with multiple-problem results. In multiple-problem analysis, we propose the use of non-parametric statistical tests given that they are less restrictive than parametric ones and they can be used over small size samples of results. As a case study, we analyze the published results for the algorithms presented in the

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Section: Introductionmentioning

confidence: 99%

A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

et al. 2008

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“…One of the main objectives in DoE is to optimize the factors by comparing and evaluating the quality of the responses. DoE has been successfully applied as a tool for the manual parameter tuning of particular computational optimization problems [31]. The initial stage of the process, experimental design, concerns the optimization of initial parameter values by applying an automated DoE to a finite training set of problem instances.…”

Section: Design Of Experiments (Doe)mentioning

confidence: 99%

Solving multi-mode time–cost–quality trade-off problems under generalized precedence relations

Khalili‐Damghani

Tavana

Abtahi³

et al. 2015

Optimization Methods and Software

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2015) Solving multi-mode time-cost-quality trade-off problems under generalized precedence relations, Optimization Methods and Software, 30:5, 965-1001, In this paper, we model a multi-mode time-cost-quality trade-off project scheduling problem under generalized precedence relations using mixed-integer mathematical programming. Several solution procedures, including the classical epsilon-constraint, the efficient epsilon-constraint method, dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO), and the multi-start partial bound enumeration algorithm, are provided to solve the proposed model. Several test problems are simulated and solved with the four methods and the performance of the methods are compared according to a set of accuracy and diversity comparison metrics. Additional analyses and tests are performed on the generated Pareto fronts of the solution procedures. Computational experiments are conducted to determine the validity and the efficiency of the DSAMOPSO method. The results show that this method outperforms the other three methods. We also carry out a sensitivity analysis of the DSAMOPSO algorithm to study the effects of parameter changes on the CPU time.

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“…SPOT was successfully applied in the fields of bioinformatics [79,33,32], environmental engineering [48,30], shipbuilding [72], fuzzy logic [82], multimodal optimization [68], statistical analysis of algorithms [50,78], multicriteria optimization [80], genetic programming [51], particle swarm optimization [9,49], automated and manual parameter tuning [31,74,42,43], graph drawing [77,65], aerospace and shipbuilding industry [63], mechanical engineering [56], and chemical engineering [39]. Bartz-Beielstein [3] collects publications related to the sequential parameter optimization.…”

Section: Sequential Parameter Optimization Toolboxmentioning

confidence: 99%

Experimental Analysis of Optimization Algorithms: Tuning and Beyond

Bartz-Beielstein

Preuß

2013

Natural Computing Series

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This chapter comprises the essence of several years of tutorials the authors gave on experimental research in evolutionary computation. We highlight the renaissance of experimental techniques also in other fields to especially focus on the specific conditions of experimental research in computer science, or more concrete, metaheuristic optimization. The experimental setup is discussed together with the pitfalls awaiting the unexperienced (and sometimes even the experienced). We present a severity criterion as a meta-statistical concept for evaluating statistical inferences, which can be used to avoid fallacies, i.e., misconceptions resulting from incorrect reasoning in argumentation caused by floor or ceiling effects. The sequential parameter optimization is discussed as a meta-statistical framework which integrates concepts such as severity. Parameter tuning is considered as a relatively new tool in method design and analysis, and it leads to the question of adaptability of optimization algorithms. Another branch of experimentation aims for attaining more concrete problem knowledge, we may term it 'exploratory landscape analysis', containing sample and visualization techniques that are often applied but not seen as being a methodological contribution. However, this chapter is not only a renarration of well known facts. We also try a look into the future to estimate what the hot topics of methodological research will be in the next years and what changes we may expect for the whole community.

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An experimental analysis of evolution strategies and particle swarm optimisers using design of experiments

Cited by 16 publications

References 5 publications

A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization

Solving multi-mode time–cost–quality trade-off problems under generalized precedence relations

Experimental Analysis of Optimization Algorithms: Tuning and Beyond

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