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 exploringthe search space and keeping diversity, this makes PCA a promising algorithm for solving DMOO problems.
|Title of host publication||GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion|
|Place of Publication||New York, USA|
|Publisher||Association for Computing Machinery|
|Number of pages||2|
|Publication status||Published - 13 Jul 2019|
- 2D Hexagonal grid
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 '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 195-196). Association for Computing Machinery. https://doi.org/10.1145/3319619.3322030