Particle Swarm Optimization - A Survey <s> Speciation <s> This paper reports further simplification and improvement of a modified particle swarm optimizer (PSO) called the multi-species particle swarm optimizer (MSPSO) proposed by the author. MSPSO extends the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tries to find out the global or local optima of that area. Therefore it can be used to locate all the global minima of multi-modal functions in parallel. The previous version of MSPSO relies strongly on the inertia-weight annealing and its performance depends on the annealing schedule. In this paper, instead, we use the constriction factor proposed by Clerc. Our new MSPSO could locate, for example, all 18 global optima of the two-dimensional Shubert function, yet it is free from annealing-schedule optimization of the inertia weight. <s> BIB001 </s> Particle Swarm Optimization - A Survey <s> Speciation <s> This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighborhood best values for solving multimodal optimization problems and for tracking multiple optima in a dynamic environment. In the proposed species-based particle swam optimization (SPSO), the swarm population is divided into species subpopulations based on their similarity. Each species is grouped around a dominating particle called the species seed. At each iteration step, species seeds are identified from the entire population, and then adopted as neighborhood bests for these individual species groups separately. Species are formed adaptively at each step based on the feedback obtained from the multimodal fitness landscape. Over successive iterations, species are able to simultaneously optimize toward multiple optima, regardless of whether they are global or local optima. Our experiments on using the SPSO to locate multiple optima in a static environment and a dynamic SPSO (DSPSO) to track multiple changing optima in a dynamic environment have demonstrated that SPSO is very effective in dealing with multimodal optimization functions in both environments <s> BIB002
Introduction of species to the particles, namely speciation is another technique for maintaining the particle diversity. Iwamatsu BIB001 proposed the multi-species PSO (MSPSO), and independently, Parrot and Li BIB002 proposed the speciesbased PSO (SPSO). In these models, several fittest particles in the swarm will become the species seeds and will include other particles in the neighborhood to its species. Additionally, the seed particle will act as the lbest of each species neighborhood. In both MSPSO and SPSO, isolation of species and gradual migration of particles upon respeciation contributed to efficient search for the global optimum of multimodal objective functions. Also, SPSO has been modified for tracking multiple optima of a dynamically changing multimodal objective functions BIB002 .