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<p>The Multi-Objective Giant Pacific Octopus Optimizer&#xa0;(MOGPOO), multiple objectives variant of the newly developed&#xa0;Giant Pacific&#xa0;Octopus Optimizer (GPOO), is described in this research as a method for tackling various issues in many industries. The multi-objective design of this algorithm has not been studied in the literature due to the algorithm model’s uniqueness. The multi-objective optimization (MO) approach is suggested to handle the multi-objective problems that develop in today’s world as technical issues get ever more complicated. Many techniques frequently encounter substandard solutions when evaluating MO&#xa0;problems, as opposed to solving properly approximated functions of Pareto optimal solutions in targets. The GPOO, which mimics the octopus’s predatory behavior, performs better than other multi-objective algorithms. In a multi-objective foraging environment, the archive was utilized to imitate octopus predatory behavior and establish social hierarchies. The MOGPOO approach is designed with multi-objective formulations to preserve and guarantee enhanced coverage of optimum solutions across all objectives. The proposed method is assessed on a set of standard unconstrained, constrained test functions, IEEE Congress on Evolutionary Computation held in 2020 (CEC2020), non-parametric tests and real-world engineering challenges. It is then compared with many popular multi-objectives state-of-the-art competing algorithms. The results, both qualitative and quantitative, show that the proposed algorithm might yield highly competitive outcomes that surpass those of current algorithms.</p>

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Multi-objective giant Pacific octopus optimizer

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Pham Vu Hong Son,
  • Luu Ngoc Quynh Khoi

摘要

The Multi-Objective Giant Pacific Octopus Optimizer (MOGPOO), multiple objectives variant of the newly developed Giant Pacific Octopus Optimizer (GPOO), is described in this research as a method for tackling various issues in many industries. The multi-objective design of this algorithm has not been studied in the literature due to the algorithm model’s uniqueness. The multi-objective optimization (MO) approach is suggested to handle the multi-objective problems that develop in today’s world as technical issues get ever more complicated. Many techniques frequently encounter substandard solutions when evaluating MO problems, as opposed to solving properly approximated functions of Pareto optimal solutions in targets. The GPOO, which mimics the octopus’s predatory behavior, performs better than other multi-objective algorithms. In a multi-objective foraging environment, the archive was utilized to imitate octopus predatory behavior and establish social hierarchies. The MOGPOO approach is designed with multi-objective formulations to preserve and guarantee enhanced coverage of optimum solutions across all objectives. The proposed method is assessed on a set of standard unconstrained, constrained test functions, IEEE Congress on Evolutionary Computation held in 2020 (CEC2020), non-parametric tests and real-world engineering challenges. It is then compared with many popular multi-objectives state-of-the-art competing algorithms. The results, both qualitative and quantitative, show that the proposed algorithm might yield highly competitive outcomes that surpass those of current algorithms.