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This chapter describes the remaining part of the population-based meta-heuristic algorithms for optimization in various fields of science and technology. Meta-heuristic algorithms have great significance, and they are preferred in multi-objective optimization of complex problems. Nature-inspired algorithms mimic the optimization exhibited by the creatures in this world to solve their community problems for survival. Nevertheless, these algorithms have their own set of advantages and disadvantages. In this chapter, we aim to perform an in-depth comparison of population-based meta-heuristics algorithms to examine the dialectical relationship of benefits and drawbacks associated with each approach.

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Meta-heuristic Algorithms as an Optimizer: Prospects and Challenges (Part II)

  • 章节

  • 发表日期：

    2025年

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  • Ata Jahangir Moshayedi,
  • Seyed Taha Mousavi Nasab,
  • Zeashan Hameed Khan,
  • Amir Sohail Khan

摘要

This chapter describes the remaining part of the population-based meta-heuristic algorithms for optimization in various fields of science and technology. Meta-heuristic algorithms have great significance, and they are preferred in multi-objective optimization of complex problems. Nature-inspired algorithms mimic the optimization exhibited by the creatures in this world to solve their community problems for survival. Nevertheless, these algorithms have their own set of advantages and disadvantages. In this chapter, we aim to perform an in-depth comparison of population-based meta-heuristics algorithms to examine the dialectical relationship of benefits and drawbacks associated with each approach.