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The great interests of genetic algorithms (GA) and particle swarm optimization (PSO) in this study are chosen for psychoanalysis. This analysis will facilitate the fresh and serious researchers to offer a wider revelation of GA and PSO. The use of GA and PSO and the hybridization of GA and PSO in the previous some years to tackle different types of problems are presented with a brief description. More specially, this paper presented the existing research published between 2015 and 2022 on applications of GA and PSO and hybridization, improvement and variants of GA and PSO in a different field. The real life application of GA and PSO in the fields of health-care, environment, industry, commerce, and smart cities is tabulated. The advantages and disadvantages of GA and PSO are also discussed. The future research directions in the area of parameters for GA and PSO, population size and hybridization of GA and PSO are also presented. This review will be supportive for interested and future investigators in the area of GA and PSO.

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A State-of-the-Art Review on Genetic Algorithm and Particle Swarm Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • Rekha Rani,
  • Vanita Garg,
  • Sarika Jain

摘要

The great interests of genetic algorithms (GA) and particle swarm optimization (PSO) in this study are chosen for psychoanalysis. This analysis will facilitate the fresh and serious researchers to offer a wider revelation of GA and PSO. The use of GA and PSO and the hybridization of GA and PSO in the previous some years to tackle different types of problems are presented with a brief description. More specially, this paper presented the existing research published between 2015 and 2022 on applications of GA and PSO and hybridization, improvement and variants of GA and PSO in a different field. The real life application of GA and PSO in the fields of health-care, environment, industry, commerce, and smart cities is tabulated. The advantages and disadvantages of GA and PSO are also discussed. The future research directions in the area of parameters for GA and PSO, population size and hybridization of GA and PSO are also presented. This review will be supportive for interested and future investigators in the area of GA and PSO.