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A hierarchical learning based artificial bee colony algorithm for numerical global optimization and its applications

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APPLIED INTELLIGENCE 2024年第1期54卷 169-200页

作者： Zhang, Qingke Bu, Xianglong Gao, Hao Li, Tianqi Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China

The Artificial Bee Colony algorithm (ABC) is a swarm intelligence algorithm inspired by honey bee harvesting behavior. It boasts the benefits of minimal parameters and strong exploration capabilities. However, the ABC algorithm is still susceptible to local optima entrapment and lacks consideration of selection probability in the onlooker bee phase, leading to reduced convergence accuracy in later search stages. To address these issues, this paper introduces an enhanced ABC algorithm called Hierarchical Learning-based Artificial Bee Colony (HLABC). Initially, a hierarchical learning approach is devised, dividing the entire population into distinct layers based on solution quality. In this hierarchical approach, bees at lower layers can access much better advantageous information from higher layers. Secondly, the exploitation ability of onlooker bees is enhanced through novel strategies designed based on hierarchical learning. Thirdly, the exploration ability of scout bees is strengthened by implementing an opposition-based learning method. To evaluate the performance of the proposed algorithm, 69 benchmark functions from four benchmark suites (CEC2005, CEC2010, CEC2013 and CEC2022) are used to test the performance of HLABC, along with five variants of the ABC algorithm, The experimental statistical results show that the HLABC algorithm outperforms the ABC algorithm on all test problems with an average winning rate of 89%. Furthermore, to validate the performance of the HLABC algorithm in real-world optimization problems, this paper applies the HLABC algorithm to two practical applications: the deployment of wireless sensor networks (WSNs), the power scheduling problem in a smart home (PSPSH) and the multi-thresholding image segmentation (MIS). The experimental and statistical results demonstrate that HLABC is an efficient and stable optimizer. It shows better or comparable performance compared to other ABC variants when considering the quality of solutions for a sui

关键词： Artificial bee colony algorithm Hierarchical learning Numerical global optimization Wireless sensor networks deployment Power scheduling problem multi-thresholding image segmentation

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An efficient Optimization State-based Coyote Optimization Algorithm and its applications

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Zhang, Qingke Bu, Xianglong Zhan, Zhi-Hui Li, Junqing Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Coyote Optimization Algorithm (COA) has demonstrated efficient performance by utilizing the multiple pack (subpopulation) mechanism. However, the fixed number of packs and a relatively singular evolutionary strategy limit its comprehensive optimization performance. Thus, this paper proposes a COA variant, referred to as the Optimization State-based Coyote Optimization Algorithm (OSCOA). In the OSCOA algorithm, a Population Optimization State Estimation Mechanism is employed for estimating the current population optimization state. Then, the estimation result is used to guide the algorithm in setting the number of packs appropriately as well as selecting appropriate evolutionary strategies to refine search directions, thereby avoiding blind exploration. Additionally, the estimation result assists each pack in selecting suitable parents to generate pups, further improving the global search efficiency of the algorithm. To validate the effectiveness of the proposed algorithm, the OSCOA algorithm is subjected to comprehensive testing and analysis along with seven efficient optimizers on 71 benchmark functions derived from the CEC2014, CEC2017, and CEC2022 benchmark suites. The results of these extensive experiments indicate the competitive performance of OSCOA. Furthermore, to further assess the capability of the OSCOA algorithm in addressing real-world problems, two practical applications is considered: wireless sensor network deployment and image segmentation. The outcomes of these applications further confirm the efficacy and stability of the OSCOA algorithm in tackling real-world scenarios.

关键词： Meta-heuristics algorithms Coyote Optimization Algorithm Population state estimation multi-thresholding image segmentation Deployment problems of wireless sensor networks

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