Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm

TL;DR

Pontogammarus Maeoticus Swarm Optimization (PMSO) is a novel metaheuristic inspired by aquatic foraging behavior, effectively solving complex optimization problems like solar PV array configuration and benchmark functions.

Contribution

This paper introduces PMSO, a new bio-inspired metaheuristic algorithm that models aquatic foraging behavior and demonstrates its effectiveness on benchmark and real-world problems.

Findings

PMSO outperforms existing algorithms on CEC05 benchmarks.

PMSO effectively optimizes partially shaded solar PV arrays.

Adaptive neighborhood change improves search efficiency.

Abstract

Nowadays, metaheuristic optimization algorithms are used to find the global optima in difficult search spaces. Pontogammarus Maeoticus Swarm Optimization (PMSO) is a metaheuristic algorithm imitating aquatic nature and foraging behavior. Pontogammarus Maeoticus, also called Gammarus in short, is a tiny creature found mostly in coast of Caspian Sea in Iran. In this algorithm, global optima is modeled as sea edge (coast) to which Gammarus creatures are willing to move in order to rest from sea waves and forage in sand. Sea waves satisfy exploration and foraging models exploitation. The strength of sea wave is determined according to distance of Gammarus from sea edge. The angles of waves applied on several particles are set randomly helping algorithm not be stuck in local bests. Meanwhile, the neighborhood of particles change adaptively resulting in more efficient progress in searching. The proposed algorithm, although is applicable on any optimization problem, is experimented for partially shaded solar PV array. Experiments on CEC05 benchmarks, as well as solar PV array, show the effectiveness of this optimization algorithm.