An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Abstract

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

A twinning bare bones particle swarm optimization algorithm

“…The overall effectiveness (OE) [ 21 ] of TBBPSO and other algorithms in the control group is computed by results in Tables 3 and 4 . The OE is calculated by Eq 4 .…”

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

“…The top three most popular examples of SI algorithms are Particle Swarm Optimization (PSO) by [19], Ant Colony Optimization (ACO) by [20], and Artificial Bee Colony (ABC) Algorithm by [62]. Some other SI-based algorithms that have their place in the literature regardless of their performance and originality include the Cuckoo Search Algorithm (CS) by [25], Firefly Algorithm (FA) by [63], COOT bird [64], Krill Herd (KH) by [31], Cat Swarm Optimization (CSO) by [65], Bat Algorithm (BA) by [66], Symbiotic Organisms Search (SOS) [67], Grey Wolf Optimizer (GWO) by [32], Moth-Flame Optimization (MFO) Algorithm checked by [68,69], Virus Colony Search (VCS) [70], Whale Optimization Algorithm (WOA)checked by [71,72], Grasshopper Optimization Algorithm (GOA) by [73], Salp Swarm Algorithm by [74,75], Crow Search Algorithm (CSA) reviewed by [76], Symbiotic Organisms Search (SOS) by [77], Reptile Search Algorithm (RSA) by [78], Butterfly Optimization Algorithm (BOA) by [79], Remora Optimization Algorithm (ROA) [80], Wild Horse Optimizer (WHO) [81], Seagull Optimization Algorithm (SOA) by [82], and Ant Lion Optimizer (ALO) reviewed by [83]. The third category is the Physics-based algorithms, which refers to the algorithms inspired from chemical rules or physical phenomena.…”

Migration-Based Moth-Flame Optimization Algorithm

“…In BFGSOLMFO, Zhang et al [111] introduced orthogonal learning (OL) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) to the MFO to enhance the solution quality of the MFO. Nadimi-Shahraki et al [112] proposed an improved moth-flame optimization (I-MFO) algorithm to evade the local optima trapping and premature convergence by adding a memory mechanism and taking advantage of the adapted wandering around search (AWAS) strategy. This algorithm is designed to solve the numerical and mechanical engineering problems.…”