On the exploration and exploitation in popular swarm-based metaheuristic algorithms

Hussain, Kashif; Salleh, Mohd Najib Mohd; Cheng, Shi; Shi, Yuhui

doi:10.1007/s00521-018-3592-0

Cited by 227 publications

(97 citation statements)

References 27 publications

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“…In swarm intelligence methods, the population diversity is a measurement which evidences the performance of an algorithm, through the distribution of generated solutions [98,99]. This principle is significantly important to analyze the behavior of each solution in a swarm as well as the swarm as a whole.…”

Section: Exploration/exploitation Balancementioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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Section: Exploration/exploitation Balancementioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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“…In order to better understand why the algorithm has a good or a bad optimization effect on the optimization problem, literatures [59], [60] attempt to reveal the search process of the algorithm from the search space's exploitation rate and exploration rate. To evaluate the exploitation and exploration abilities of EGWO, this study draws two from three types of benchmark functions, namely F1 and F3 of unimodal type, F5 and F6 of multimodal type, and F9 and F11 of multimodal fixed dimension type.…”

Section: ) Analysis Of Exploitation and Explorationmentioning

confidence: 99%

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Wang

Xie

2020

IEEE Access

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Aiming at the difficulty of deploying wireless sensor networks (WSNs) on three-dimensional (3D) surfaces, based on the grey wolf optimizer (GWO), an enhanced version of the grey wolf optimizer is proposed for deploying WSNs on 3D surfaces, namely the enhanced grey wolf optimizer (EGWO), which is characterized by enhanced exploitation and exploration ability of the algorithm. The novelty of EGWO is that the grey wolf population is divided into two parts, one part is responsible for the outer-layer encircle and the other is responsible for the inner-layer encircle, and the introduction of Tent mapping. The purpose of this is to enhance the exploitation and exploration ability of the algorithm respectively, so as to improve the convergence and optimization precision of the algorithm. In addition, in terms of WSN deployment in 3D surfaces, this paper improves the means of determining the perceived blind zone. Meanwhile, a novel method to calculate the WSNs coverage area of simple and complex 3D surfaces is presented by combining the grid and integral of the 3D surfaces. The EGWO is favorably compared with the GWO and three existing variants of the grey wolf optimizer when testing on 12 well-known benchmark functions. The simulation experiment results show that compared with the existing algorithms, EGWO can provide a very competitive search result in terms of optimization precision and convergence performance. Finally, this paper applies EGWO to the 3D surface deployment of WSN. Simulations show that compared with the other three deployment algorithms, EGWO can improve the network coverage of WSN, which can save network deployment costs. In addition, the probability of network connectivity deployed by EGWO is higher, that is, EGWO can provide a better deployment solution. INDEX TERMS Wireless sensor network, three-dimensional surface deployment, perceived blind zone, grid and integration, optimization, grey wolf optimizer, inner-layer encircle.

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“…To better analyze search behavior of any optimization technique, it is essential to gauge exploration and exploitation, as the two contradicting characteristics influence performance. According to Hussain et al [39], it is possible to measure explorative and exploitative capabilities once diversity in population is calculated. The research proposed dimension-wise diversity measurement as an effective technique which is employed in this current study.…”

Section: ) Exploration-exploitation Measurementmentioning

confidence: 99%

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

2019

Self Cite

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Harris' hawk optimization (HHO) is a recent addition to population-based metaheuristic paradigm, inspired from hunting behavior of Harris' hawks. It has demonstrated promising search behavior while employed on various optimization problems, however the diversity of search agents can be further enhanced. This paper represents a novel modified variant with a long-term memory concept, hence called long-term memory HHO (LMHHO), which provides information about multiple promising regions in problem landscape, for improvised search results. With this information, LMHHO maintains exploration up to a certain level even until search termination, thus produces better results than the original method. Moreover, the study proves that appropriate tools for in-depth performance analysis can help improve search efficiency of existing metaheuristic algorithms by making simple yet effective modification in search strategy. The diversity measurement and exploration-exploitation investigations prove that the proposed LMHHO maintains trade-off balance between exploration and exploitation. The proposed approach is investigated on high-dimensional numerical optimization problems, including classic benchmark and CEC'17 functions; also, on optimal power flow problem in power generation system. The experimental study suggests that LMHHO not only outperforms the original HHO but also various other established and recently introduced metaheuristic algorithms. Although, the research can be extended by implementing more efficient memory archive and retrieval approaches for enhanced results. INDEX TERMS Diversity measurement, exploration-exploitation, long-term memory, Harris' hawk optimization, optimal power flow.

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On the exploration and exploitation in popular swarm-based metaheuristic algorithms

Cited by 227 publications

References 27 publications

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

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