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A physarum-inspired competition algorithm for solving discrete multi-objective optimization problems

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Abstract

Many real-world problems can be naturally formulated as discrete multi-objective optimization (DMOO) problems. In this research we propose a novel bio-inspired Physarum competition algorithm (PCA) to tackle DMOO problems by modelling the Physarum discrete motility over a hexagonal cellular automaton. Our algorithm is based on the chemo-attraction forces towards food resources (Objective Functions) and the repulsion negative forces between the competing Physarum. Numerical experimental work clearly demonstrated that our PCA algorithm had the best performance for the spread indicator against three state-of-the-art evolutionary algorithms, and its effectiveness in terms of commonly used metrics. These results have indicated the superiority of PCA in exploring the search space and keeping diversity, this makes PCA a promising algorithm for solving DMOO problems.

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Awad, A., Usman, M., Lusseau, D., Coghill, G. M., & Pang, W. (2019). A physarum-inspired competition algorithm for solving discrete multi-objective optimization problems. In GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 195–196). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322030

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