Learning-based elephant herding optimization algorithm for solving numerical optimization problems

Li, Wei; Wang, Gai-Ge; Alavi, Amir H.

doi:10.1016/j.knosys.2020.105675

Cited by 96 publications

(36 citation statements)

References 76 publications

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“…At present, many single objective optimization problems in the optimization field have become the focus of research, such as workshop scheduling problems [45][46][47][48][49] and numerical optimization problems [50,51]. Most of these can be solved by classical algorithms and their improved versions, such as artificial bee colony algorithm (ABC) [47,48,52,53], particle swarm optimization (PSO) [51,54], monarch butterfly optimization (MBO) [55][56][57][58], ant colony optimization (ACO) [59,60], krill herd algorithm (KH) [52,[61][62][63][64], elephant herding optimization (EHO) [65][66][67], and other metaheuristic algorithms [68][69][70][71][72][73][74][75][76][77]. However, there are some problems in many-objective optimizations, which cannot be solved by single objective techniques.…”

Section: The Background Of Maopsmentioning

confidence: 99%

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Gui

Wang

Wang³

2021

Mathematics

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Due to the complexity of many-objective optimization problems, the existing many-objective optimization algorithms cannot solve all the problems well, especially those with complex Pareto front. In order to solve the shortcomings of existing algorithms, this paper proposes a coevolutionary algorithm based on dynamic learning strategy. Evolution is realized mainly through the use of Pareto criterion and non-Pareto criterion, respectively, for two populations, and information exchange between two populations is used to better explore the whole objective space. The dynamic learning strategy acts on the non-Pareto evolutionary to improve the convergence and diversity. Besides, a dynamic convergence factor is proposed, which can be changed according to the evolutionary state of the two populations. Through these effective heuristic strategies, the proposed algorithm can maintain the convergence and diversity of the final solution set. The proposed algorithm is compared with five state-of-the-art algorithms and two weight-sum based algorithms on a many-objective test suite, and the results are measured by inverted generational distance and hypervolume performance indicators. The experimental results show that, compared with the other five state-of-the-art algorithms, the proposed algorithm achieved the optimal performance in 47 of the 90 cases evaluated by the two indicators. When the proposed algorithm is compared with the weight-sum based algorithms, 83 out of 90 examples achieve the optimal performance.

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Section: The Background Of Maopsmentioning

confidence: 99%

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Gui

Wang

Wang³

2021

Mathematics

Self Cite

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“…Some of the already implemented learning methods to improve evolutionary algorithms are also inspired by nature, but, in this case, they mimic a herd or colony's behavior. We can mention some swarm intelligence methods as examples, such as chaotic krill herds (Wang, G., Guo, Gandomi, Hao, and Wang, H., 2014), elephant herds (Li,, Wang, and Alavi,, 2020), bee colonies (Wang and Ji, 2017), monarch butterflies , and moth colonies (Wang, 2016). These methods simulate the clustering behavior of chaotic krills, elephants, and also other insect behaviors such as migration and phototaxis.…”

Section: Related Workmentioning

confidence: 99%

EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points

2020

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Multi-Objective and Many-objective Optimization problems have been extensively solved through evolutionary algorithms over a few decades. Despite the fact that NSGA-II and NSGA-III are frequently employed as a reference for a comparative evaluation of new evolutionary algorithms, the latter is proprietary. In this paper, we used the basic framework of the NSGA-II, which is very similar to the NSGA-III, with significant changes in its selection operator. We took the first front generated at the non-dominating sort procedure to obtain nonnegative and nonrepeated extreme points. This opensource version of the NSGA-III is called EF1-NSGA-III, and its implementation does not start from scratch; that would be reinventing the wheel. Instead, we took the NSGA-II code from the authors in the repository of the Kanpur Genetic Algorithms Laboratory to extend the EF1-NSGA-III. We then adjusted its selection operator from diversity, based on the crowding distance, to the one found on reference points and preserved its parameters. After that, we continued with the adaptive EF1-NSGA-III (A-EF1-NSGA-III), and the efficient adaptive EF1-NSGA-III (A2-EF1-NSGA-III), while also contributing to explain how to generate different types of reference points. The proposed algorithms resolve optimization problems with constraints of up to 10 objective functions. We tested them on a wide range of benchmark problems, and they showed notable improvements in terms of convergence and diversity by using the Inverted Generational Distance (IGD) and the HyperVolume (HV) performance metrics. The EF1-NSGA-III aims to resolve the power consumption for Centralized Radio Access Networks and the BiObjective Minimum DiameterCost Spanning Tree problems.

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“…ird, to improve the internal structure of EHO, this part of the research focused on proposing adaptive operators and stagnation prevention mechanisms. Li et al [62] introduced a global speed strategy based on EHO to assign travel speed to each elephant and achieved good results on CEC 2014. Ismaeel et al [63] addressed the problem of unreasonable convergence to the origin in EHO by improving the cladistic update operator and separation operator, achieving the balance between exploration and exploitation.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Perturbation Specular Reflection Learning and Golden‐Sine‐Mechanism‐Based Elephant Herding Optimization for Global Optimization Problems

Duan

Liu

et al. 2021

Computational Intelligence and Neuroscience

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Elephant herding optimization (EHO) has received widespread attention due to its few control parameters and simple operation but still suffers from slow convergence and low solution accuracy. In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden-sine-mechanism-based EHO (SRGS-EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best-positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS-EHO is also applied to solve two classic engineering problems with a constrained search space (pressure-vessel design problem and tension-/compression-string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production.

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Learning-based elephant herding optimization algorithm for solving numerical optimization problems

Cited by 96 publications

References 76 publications

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points

Gaussian Perturbation Specular Reflection Learning and Golden‐Sine‐Mechanism‐Based Elephant Herding Optimization for Global Optimization Problems

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