Marine Predators Algorithm: A nature-inspired metaheuristic

By Afshin Faramarzi, Mohammad Heidarinejad, Seyedali Mirjalili, Amir H. Gandomi. Expert systems with applications, 2020

2,143 citations recorded by the source corpus.

Abstract This paper presents a nature-inspired metaheuristic called Marine Predators Algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Levy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems. This paper evaluates the MPA's performance on twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape, three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy performance. MPA is compared with three classes of existing optimization methods, including (1) GA and PSO as the most well-studied metaheuristics, (2) GSA, CS and SSA as almost recently developed algorithms and (3) CMA-ES, SHADE and LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-cnEpSin. The source code is publicly available at: https://github.com/afshinfaramarzi/Marine-Predators-Algorithm, http://built-envi.com/portfolio/marine-predators-algorithm/, https://www.mathworks.com/matlabcentral/fileexchange/74578-marine-predators-algorithm-mpa, and http://www.alimirjalili.com/MPA.html.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7495f2ca94786cdf0fe11db0e22647e7c1df8c87

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Citations in the corpus

Zebra Optimization Algorithm: A New Bio-inspired Optimization Algorithm for Solving Optimization Algorithm (2022, methodology): “The strategy of living organisms when hunting and trapping prey has been the main idea of various metaheuristic algorithms such as Grey Wolf Optimizer (GWO) [10], Spotted Hyena Optimizer (SHO) [11], Whale Optimization Algorithm (WOA) [12], Chameleon Swarm Algorithm (CSA) [13], and Marine Predator Al”
Dung beetle optimizer: a new meta-heuristic algorithm for global optimization (2022, methodology): “There are several optimization algorithms previously applied to the welded beam design problem such as the HHO algorithm [17], GSA [46], WOA [16], MVO algorithm [40], MPA [48], CPSO algorithm [49], HS algorithm [50], and SSA [22].”
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems (2021, background): “Some other swarm-based algorithms are Raccoon Optimization Algorithm (ROA) [11], Teaching-Learning Based Optimization (TLBO) [12], Crow Search Algorithm (CSA) [13], Grasshopper Optimization Algorithm (GOA) [14], Tunicate Swarm Algorithm (TSA) [15], and Marine Predators Algorithm (MPA) [16].”
Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer (2022, background): “Although, studies have shown that quite a number of the proposed metaheuristic optimization methods are generally inspired by the foraging and hunting behaviors of animals in their natural habitat [25], and more so despite the interesting survival life cycle of some animals in the wild such as the g”
Prairie Dog Optimization Algorithm (2022, neutral): “70E-12 NA NA NA NA PSOSCALF [73] 49 19 16 43 2.”