A Survey: Particle Swarm Optimization Based Algorithms to Solve Premature Convergence Problem

Abstract

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method based on the social behaviors of birds flocking or fish schooling. Although PSO is represented in solving many well-known numerical test problems, but it suffers from the premature convergence. A number of basic variations have been developed due to solve the premature convergence problem and improve quality of solution founded by the PSO. This study presents a comprehensive survey of the various PSO-based algorithms. As part of this survey, we include a classification of the approaches and we identify the main features of each proposal. In the last part of the study, some of the topics within this field that are considered as promising areas of future research are listed.

A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis

“…Nakisa et al [30] noted that the conventional PSO algorithm satisfactorily finds optimal solutions but often fails to find the universally applicable solution. Therefore, whether PSO reaches the global or local optimum cannot be known.…”

“…Nakisa et al [30] state that premature convergence will always prevail in conventional PSO because the entire search-space must be matched to obtain the optimal resolution. As the goals of maintaining high diversity and achieving fast convergence are partially antagonistic, it makes perfect sense to reduce the number of sub-optimal solutions found in the optimization.…”

“…These authors state that to avoid premature convergence and maintain an active particle population, we should mainly adjust the distance between the particles and the optimal position. Nakisa et al [30] addressed the problem of non-scanning of potential areas when the swarm becomes trapped in a sub-optimal zone. In this case, the swarm cannot explore other promising areas.…”

“…Therefore, whether PSO reaches the global or local optimum cannot be known. If PSO fails to find the global optima, the swarm will become trapped into premature convergence [30].…”

“…To remedy premature convergence and other problems in PSO, researchers have modified the PSO parameters, incorporated mutations within the PSO framework, hybridized PSO and GA, or adopted other evolutionary techniques [30].…”