Analyzing Adaptive Parameter Landscapes in Parameter Adaptation Methods for Differential Evolution

Since the scale factor and the crossover rate significantly influence the performance of differential evolution (DE), parameter adaptation methods (PAMs) for the two parameters have been well studied in the DE community. Although PAMs can sufficiently improve the effectiveness of DE, PAMs are poorly understood (e.g., the working principle of PAMs). One of the difficulties in understanding PAMs comes from the unclarity of the parameter space that consists of the scale factor and the crossover rate. This paper addresses this issue by analyzing adaptive parameter landscapes in PAMs for DE. First, we propose a concept of an adaptive parameter landscape, which captures a moment in a parameter adaptation process. For each iteration, each individual in the population has its adaptive parameter landscape. Second, we propose a method of analyzing adaptive parameter landscapes using a 1-step-lookahead greedy improvement metric. Third, we examine adaptive parameter landscapes in PAMs by using the proposed method. Results provide insightful information about PAMs in DE.Comment: This is an accepted version of a paper published in the proceedings of GECCO 202

How distance based parameter adaptation affects population diversity

This paper discusses the effect of distance based parameter adaptation on the population diversity of the Success-History based Adaptive Differential Evolution (SHADE). The distance-based parameter adaptation was designed to promote exploration over exploitation and provide better search capabilities of the SHADE algorithm in higher dimensional objective spaces. The population diversity is recorded on the 15 test functions from the CEC 2015 benchmark set in two-dimensional settings, 10D and 30D, to provide the empiric evidence of a beneficial influence of the distance based parameter adaptation in comparison with the objective function value based approach. © 2018, Springer International Publishing AG, part of Springer Nature.Ministry of Education; CA15140, COST, European Cooperation in Science and Technology; IC406, COST, European Cooperation in Science and Technology; IGA/CebiaTech/2018/003; MSMT-7778/2014; LO1303; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development Fund; COST, European Cooperation in Science and TechnologyMinistry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [Action CA15140, Action IC406

On the prolonged exploration of distance based parameter adaptation in SHADE

In this paper, a prolonged exploration ability of distance based parameter adaptation is subject to a test via clustering analysis of the population in Success-History based Adaptive Differential Evolution (SHADE). The comparative study is done on the CEC 2015 benchmark set in two dimensional settings – 10D and 30D. It is shown, that the exploration phase of distance based adaptation in SHADE (Db_SHADE) lasts for more generations and therefore avoids the premature convergence into local optima. © Springer International Publishing AG, part of Springer Nature 2018.IC1406, COST, European Cooperation in Science and Technology; CA15140, COST, European Cooperation in Science and Technology; IGA/CebiaTech/2018/003; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; COST, European Cooperation in Science and Technology; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development FundMinistry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [CA15140, IC1406

A lightweight SHADE-based algorithm for global optimization - liteSHADE

In this paper, a novel lightweight version of the Successful-History based Adaptive Differential Evolution (SHADE) is presented as the first step towards a simple, user-friendly, metaheuristic algorithm for global optimization. This simplified algorithm is called liteSHADE and is compared to the original SHADE on the CEC2015 benchmark set in three dimensional settings – 10D, 30D and 50D. The results support the idea, that simplification may lead to a successful and understandable algorithm with competitive or even better performance. © Springer Nature Switzerland AG 2020

Analysis of Evolutionary Algorithms Using Multi-objective Parameter Tuning

Evolutionary Algorithms (EAs) and other metaheuristics are greatly affected by the choice of their parameters, not only as regards the precision of the solutions found, but also for repeatability, robustness, speed of convergence, and other properties. Most of these performance criteria are often conflicting with one another. In our work, we see the problem of EAs' parameter selection and tuning as a multi-objective optimization problem, in which the criteria to be optimized are precision and speed of convergence. We propose EMOPaT (Evolutionary Multi-Objective Parameter Tuning), a method that uses a well-known multi-objective optimization algorithm (NSGA-II) to find a front of non-dominated parameter sets which produce good results according to these two metrics. By doing so, we can provide three kinds of results: (i) a method that is able to adapt parameters to a single function, (ii) a comparison between Differential Evolution (DE) and Particle Swarm Optimization (PSO) that takes into consideration both precision and speed, and (iii) an insight into how parameters of DE and PSO affect the performance of these EAs on different benchmark functions