Abstract
In real world situations, decision makers prefer to have multiple optimal solutions before making a final decision. Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). Our GPSA is developed based on the concept of “particle clustering in the absence of clustering procedures”. Specifically, it simply replaces the global feedback term in classical particle swarm optimization (PSO) with an inverse-square gravitational force term between the particles. The gravitational force mutually attracts and repels the particles, enabling them to autonomously and dynamically generate sub-swarms in the absence of algorithmic clustering procedures. Most of the sub-swarms gather at the nearby global optima, but a small number of particles reach the distant optima. The niching behavior of our GPSA was tested first on simple MMO problems, and then on twenty MMO benchmark functions. The performance indices (peak ratio and success rate) of our GPSA were compared with those of existing niching PSOs (ring-topology PSO and fitness Euclidean-distance ratio PSO). The basic performance of our GPSA was comparable to that of the existing methods. Furthermore, an improved GPSA with a dynamic parameter delivered significantly superior results to the existing methods on at least 60% of the tested benchmark functions.
Funder
Japan Society for the Promotion of Science
Publisher
Public Library of Science (PLoS)
Reference43 articles.
1. Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications;X Li;IEEE Transactions on Evolutionary Computation,2017
2. The second Toyota paradox: how delaying decisions can make better cars faster;A Ward;Sloan Management Review,1995
3. Induction motor design for electric vehicle using a niching genetic algorithm;Cho Dong-Hyeok;IEEE Transactions on Industry Applications,2001
4. Sun C, Liang H, Li L, Liu D. Clustering with a Weighted Sum Validity Function Using a Niching PSO Algorithm. In: 2007 IEEE International Conference on Networking, Sensing and Control. London, UK: IEEE; 2007. p. 368–373.
5. Kronfeld M, Dräger A, Aschoff M, Zell A. On the benefits of multimodal optimization for metabolic network modeling. In: GCB 2009—German Conference on Bioinformatics 2009. Bonn: Gesellschaft für Informatik e.V.; 2009. p. 191–200.
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