Particle Swarm Optimization in High-Dimensional Bounded Search Spaces

Helwig, Sabine; Wanka, Rolf

doi:10.1109/sis.2007.368046

Cited by 65 publications

(42 citation statements)

References 8 publications

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“…In some 500-dimensional cases, Random-A was not able to provide satisfactory results for the optimization problem under consideration. It was observed earlier that Random can distract particles from the boundary [6] which might be a reason for the bad performance of Random-A on some functions. Note that in high-dimensional spaces, most of the volume is located near the boundary (see, e.g., Theorem 3.1. in [6]), and a search algorithm should therefore be able to explore boundary regions.…”

Section: A Experiments 1: Comparison With a Standard Psomentioning

confidence: 94%

“…It was observed earlier that Random can distract particles from the boundary [6] which might be a reason for the bad performance of Random-A on some functions. Note that in high-dimensional spaces, most of the volume is located near the boundary (see, e.g., Theorem 3.1. in [6]), and a search algorithm should therefore be able to explore boundary regions. Table II lists the results of the one-sided Wilcoxon rank sum test applied on the outcome of this experiment.…”

Section: A Experiments 1: Comparison With a Standard Psomentioning

confidence: 94%

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Particle Swarm Optimization with Velocity Adaptation

Helwig

Neumann

Wanka

2009

2009 International Conference on Adaptive and Intelligent Systems

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Abstract-Particle swarm optimization (PSO) algorithms have gained increasing interest for dealing with continuous optimization problems in recent years. Often such problems involve boundary constraints. In this case, one has to cope with the situation that particles may leave the feasible search space. To deal with such situations different bound handling methods have been proposed in the literature and it has been observed that the success of PSO algorithms depends on a large degree on the used bound handling method. In this paper, we propose an alternative approach to cope with bounded search spaces. The idea is to introduce a velocity adaptation mechanism into PSO algorithms that is similar to step size adaptation used in evolution strategies. Using this approach we show that the bound handling method becomes less important for PSO algorithms and that using velocity adaptation leads to better results for a wide range of benchmark functions.

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Section: A Experiments 1: Comparison With a Standard Psomentioning

confidence: 94%

Section: A Experiments 1: Comparison With a Standard Psomentioning

confidence: 94%

Particle Swarm Optimization with Velocity Adaptation

Helwig

Neumann

Wanka

2009

2009 International Conference on Adaptive and Intelligent Systems

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“…In our tests the Particle Sw which is a global search meth method representing probabil global search for optimal so group of algorithms motivate used and well described in [6,7,8,9]. Let us recall basi algorithm.…”

Section: Particle Swarm Optimmentioning

confidence: 99%

Multi-criteria optimization strategies for production chains

et al. 2016

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“…Helwig and Wanka investigated four approaches for managing boundary constraints when solving high-dimensional singleobjective optimization problem (SOOP) [13]. Chu et al investigated the effect of the three boundary handling techniques mentioned above for high dimensional SOOP and high dimensional composite SOOP.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Dynamic Multi-Objective Optimization Using PSO

Helbig

Engelbrecht

2013

Metaheuristics for Dynamic Optimization

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Abstract. Dynamic multi-objective optimization problems occur in many situations in the real world. These optimization problems do not have a single goal to solve, but many goals that are in conflict with one another -improvement in one goal leads to deterioration of another. Therefore, when solving dynamic multi-objective optimization problem, an algorithm attempts to find the set of optimal solutions, referred to as the Pareto-optimal front. Each dynamic multi-objective optimization problem also has a number of boundary constraints that limits the search space. When the particles of a particle swarm optimization (PSO) algorithm move outside the search space, an approach should be followed to manage violation of the boundary constraints. This chapter investigates the effect of various approaches to manage boundary constraint violations on the performance of the dynamic Vector Evaluated Particle Swarm optimization (DVEPSO) algorithm when solving DMOOP. Furthermore, the performance of DVEPSO is compared against the performance of three other state-of-the-art dynamic multi-objective optimization algorithms. IntroductionMany problems in the real-world change over time and require more than one goal to be optimized. However, these goals, or objectives, are normally in conflict with one another, where an improvement in one objective results in deterioration of another objective. Therefore, a single solution does not exist and the goal becomes

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Particle Swarm Optimization in High-Dimensional Bounded Search Spaces

Cited by 65 publications

References 8 publications

Particle Swarm Optimization with Velocity Adaptation

Particle Swarm Optimization with Velocity Adaptation

Multi-criteria optimization strategies for production chains

Dynamic Multi-Objective Optimization Using PSO

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