2018/07/05 by Benyamin Ghojogh, Ghojogh, Benyamin, Saeed Sharifian +1
Computer Science · #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1807.01844
openalex publication_date 2018/07/05 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Nowadays, metaheuristic optimization algorithms are used to find the global\noptima in difficult search spaces. Pontogammarus Maeoticus Swarm Optimization\n(PMSO) is a metaheuristic algorithm imitating aquatic nature and foraging\nbehavior. Pontogammarus Maeoticus, also called Gammarus in short, is a tiny\ncreature found mostly in coast of Caspian Sea in Iran. In this algorithm,\nglobal optima is modeled as sea edge (coast) to which Gammarus creatures are\nwilling to move in order to rest from sea waves and forage in sand. Sea waves\nsatisfy exploration and foraging models exploitation. The strength of sea wave\nis determined according to distance of Gammarus from sea edge. The angles of\nwaves applied on several particles are set randomly helping algorithm not be\nstuck in local bests. Meanwhile, the neighborhood of particles change\nadaptively resulting in more efficient progress in searching. The proposed\nalgorithm, although is applicable on any optimization problem, is experimented\nfor partially shaded solar PV array. Experiments on CEC05 benchmarks, as well\nas solar PV array, show the effectiveness of this optimization algorithm.\n