An exploration-enhanced elephant herding optimization

Abstract

Purpose The purpose of this paper is to propose an enhanced elephant herding optimization (EEHO) algorithm by improving the exploration phase to overcome the fast-unjustified convergence toward the origin of the native EHO. The exploration and exploitation of the proposed EEHO are achieved by updating both clan and separation operators. Design/methodology/approach The original EHO shows fast unjustified convergence toward the origin specifically, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms. Furthermore, the star discrepancy measure is adopted to quantify the quality of the exploration phase of evolutionary algorithms in general. Findings In experiments, EEHO has shown a better performance of convergence rate compared with the original EHO. Reasons behind this performance are: EEHO proposes a more exploitative search method than the one used in EHO and the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Operator γ is added to EEHO assists to escape from local optima, which commonly exist in the search space. The proposed EEHO controls the convergence rate and the random walk independently. Eventually, the quantitative and qualitative results revealed that the proposed EEHO outperforms the original EHO. Research limitations/implications Therefore, the pros and cons are reported as follows: pros of EEHO compared to EHO – 1) unbiased exploration of the whole search space thanks to the proposed update operator that fixed the unjustified convergence of the EHO toward the origin and the proposed separating operator that fixed the tendency of EHO to introduce new elephants at the boundary of the search space; and 2) the ability to control exploration–exploitation trade-off by independently controverting the convergence rate and the random walk using different parameters – cons EEHO compared to EHO: 1) suitable values for three parameters (rather than two only) have to be found to use EEHO. Originality/value As the original EHO shows fast unjustified convergence toward the origin specifically, the search method adopted in EEHO is more exploitative than the one used in EHO because of the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Further, the star discrepancy measure is adopted to quantify the quality of exploration phase of evolutionary algorithms in general. Operator γ that added EEHO allows the successive local and global searching (exploration and exploitation) and helps escaping from local minima that commonly exist in the search space.

A response coordination design technology for the main loaded structures of the internal combustion engines based on NSEHO

“…28 A number of improved EHO algorithms based on chaos theory, 29 individual updating strategies, 30 Lévy flight, 31 multisearch strategy, 32 data clustering, 33 and adaptive strategies 34 are proposed to enhance the performance of original EHO. About hybrid EHO algorithms, separating operators with balanced control, 35 fuzzy logic controller, 36 constant function, 37 gray wolf optimizer (GWO), 38 and genetic algorithm (GA) 39 are combined with standard EHO to address special optimization problems in different fields. Regarding the variants, a new binary variant of EHO 40 and a multiobjective clustering EHO 41 are researched for solving binary optimization problems and multiobjective optimization problems, respectively.…”

Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

“…The Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO) algorithm, which is based on the FS algorithm, is proposed for choosing breast cancer features [28,29]. The clan informing operator and the separating operator with mutation operator are used in AMEHO to accomplish discovery and exploitation.…”

Bio-Inspired Ensemble Feature Selection (Biefs) and Ensemble Multiple Deep Learning (Emdl) Classifier for Breast Cancer Diagnosis

“…The Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO) algorithm, which is based on the FS algorithm, is proposed for choosing breast cancer features. The clan informing operator and the separating operator with mutation operator are used in AMEHO to accomplish discovery and exploitation [21,22].…”