Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm
This is an incremental contribution for researchers in optimization algorithms, offering a new bio-inspired method for solving optimization problems.
The authors tackled the problem of finding global optima in difficult search spaces by proposing Pontogammarus Maeoticus Swarm Optimization (PMSO), a metaheuristic algorithm inspired by aquatic foraging behavior, and demonstrated its effectiveness on CEC05 benchmarks and a partially shaded solar PV array.
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.