Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

Abstract

The elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.

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“…In this subsection, the proposed MRFO-PSO is further evaluated through comparing its performance with some advanced variants of MRFO reported in the literature including modified MRFO (m-MRFO) [ 67 ], and MRFO and Gaussian mutation-based elephant herding optimization for global optimization (MGEHO) [ 54 ]. The results are recorded in Table 5 using the mean value of the fitness function along with the standard devotion (STD).…”

“…Tiwari et al minimized the total operating cost for distributed generator evaluated by load dispatch [52], while Sultan et al used MRFO to solve multi-objective problems of sizing components of hybrid PV, wind turbine, and fuel cell system [53]. Simultaneously, other researchers have integrated MRFO with other algorithms like, Duan et al replaced the clan updating operator in the elephant herding optimization (EHO) method with the somersault foraging tactic of Manta rays, and enhanced the diversity of the population by the Gaussian mutation [54]. Houssein et al [55] proposed that a modified MRFO with oppositionbased learning (OBL), named MRFO-OBL, was employed to solve the problem of the image segmentation with multilevel thresholding's, where the MRFO-OBL was employed to identify the COVID-19 using chest CT images.…”

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“…The MRFO algorithm interprets food as a hub when each agent flips along somersault foraging. MRFO is a search method based on these three strategies for foraging [31] . Thus, agents can improve their exploitation capabilities by adjusting their position to the optimal solution.…”

“…It has been proposed that fuzzy inference systems can be used to model qualitative and non-numerical assertions. FIS uses fuzzy numbers and operations to express knowledge via the behavior of linguistic variables governed by “IF-THEN” rules [31] . Thus, a FIS consists of: A database of IF-THEN rules; The membership functions of all fuzzy sets featured in the rules; The decision-making model executes inference operations on the rules.…”

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“…The algorithm-specific equations then iteratively evolve candidate solutions until the termination condition is satisfied. As a result, various optimization algorithms can propose varying degrees of solution improvement [28]. Evolution, physics, and swarms are three commonly used metaheuristic algorithm types [29].…”