Enhanced Elephant Herding Optimization for Global Optimization

Abstract

The elephant herding optimization (EHO) algorithm is a relatively novel population-based optimization technique, which mimics herding behavior and can be modeled into two operators: clan updating operators and separating operators. Also, in the literature, EHO has received a great deal of attention from researchers since it was proposed applied to many application fields for its advantages of excellent global optimization ability and ease of implementation. However, there is still an insufficiency in the EHO algorithm regarding its lack of exploitation, which leads to slow convergence. In this paper, we propose three enhanced versions of EHO based on the γ value termed EEHO15, EEHO20, and EEHO25 to overcome the problems of fast unjustified convergence toward the origin of the basic EHO. The exploration/exploitation abilities of the EEHO algorithms are achieved by the updating of the two operators (clan and separation operator). To tackle this drawback, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms in general. Moreover, we utilize CEC'17 test suite benchmark functions to test the performance of the proposed three versions of EEHO against EHO, particle swarm optimization (PSO), bird swarm algorithm (BSA), and ant lion optimizer (ALO) algorithms. Eventually, the experimental results revealed that the proposed EEHO algorithms extremely obtained better results compared with other competitive algorithms.

Parameter Optimization of Support Vector Regression Using Henry Gas Solubility Optimization Algorithm

“…It has become a significant trend that meta-heuristic algorithms are introduced into SVR parameter adjustment. Mimicking ethological, biological, or physics phenomena is the main means of meta-heuristic algorithms in solving optimization problems [21]. The algorithms applied to SVR include GA [15], [22], FA [4], gray wolf optimization (GWO) [23], PSO [10], ALO [11], DA [12], SSA [13], whale optimization algorithm (WOA) [24], [25], elephant herding algorithm (EHO) [26], simulated annealing (SA) [27], etc.…”

Energy-Based Acoustic Localization by Improved Elephant Herding Optimization

“…With this procedure, it will be shown that the modified algorithm obtains up to 1 m of reduction in the localization error for lower values of noise, when considering the original version, requiring considerably less iterations (regarding the enhanced version, the error will be just slightly lower). For higher values of the noise, it replicates the performance of the original EHO [16] and its enhanced version [18]. The increase in computational effort is compensated by the reduction of the number of iterations, due to substantial increase of the convergence rate.…”

“…. , N , β = 2, ξ = 0.7, α = 0.1, population size of 100 elephant divided in 5 clans, and the maximum number of function evaluations of 3000), (3) the Enhanced EHO (EEHO) from [18], and the new iEHO presented in Section III, considering the stopping criteria from eq. 18, where f = 10 −5 .…”

“…Moreover, the authors in [17] considered enhancing the performance of the original EHO, via cultural-based, alpha-tuning, and biased initialization EHO. Another improved version of the original EHO algorithm was recently proposed in [18], where the authors noticed that there is a lack of exploitation in EHO, which leads to slow convergence. Hence, they updated the two operators (clan and separation operator) for the population regeneration in each iteration.…”

A modified elephant herd optimization algorithm to solve the single machine scheduling problems

“…Recently, Elhosseini et al [65] presented an improved EHOA to tackle the optimization problems. Ismaeel et al [66] developed an enhanced version of EHOA for global optimization. Jaiprakash & Nanda [67] applied the EHOA for solving the clustering problems.…”