[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-149777-en":3,"doc-seo-149777-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},149777,2336475104042,"Tawan","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","A Novel Whale Optimization Algorithm Based on Population Diversity Strategy","The paper develops a novel whale optimization algorithm (NWOA) by introducing a population diversity strategy into the whale optimization algorithm (WOA). It targets common meta-heuristic shortcomings of WOA, especially poor balance between exploration and exploitation and the tendency to fall into local optima. The diversity mechanism strengthens NWOA’s exploration ability across the search process and improves solution quality. Numerical experiments confirm that NWOA produces higher-quality results than baseline approaches.","A Novel Whale Optimization Algorithm Based on Population Diversity Strategy  \nKui Liu, Yuguang Wang  \nAbstract—The whale optimization algorithm (WOA) is a meta-heuristic optimization algorithm inspired by the hunting behavior of humpback whales. Due to its simplicity and ease of implementation, WOA has become a popular algorithm for solving global optimization problems. However, like other meta-heuristic optimization algorithms, WOA also has some de􀀂ciencies. In order to overcome these shortcomings, a novel whale optimization algorithm based on population diversity strategy (referred to as NWOA) is proposed in this paper. This population diversity strategy aims to enhance the exploration property of NWOA and avoid getting trapped in local optima. Numerical experiments demonstrate that the proposed method is capable of producing higher quality solutions.  \nIndex Terms—whale optimization algorithm; population diversity strategy; dynamic selective rule; swarm intelligence  \nI. INTRODUCTION  \nANY animal in nature possesses two fundamental in  \nstincts: reproduction and foraging. Reproduction ensures genetic continuity, foraging is essential for the continuation of their existence. Therefore, whether as individual animals or entire populations, having an effective foraging strategy can signi􀀂cantly increase their chances of survival. Different animals employ various foraging methods. By simulating foraging strategy, many meta-heuristic optimization algorithms [1-4] have been proposed.  \nHumpback whales primarily hunt small 􀀂sh and shrimp. When they encounter a shoal of 􀀂sh, humpback whales simply rush into the swarm and swallow the 􀀂sh and shrimp together with water. However, this hunting method is ineffective when the 􀀂sh swarm is dispersed. To address this dilemma, humpback whales have developed an effective foraging method known as bubble net feeding. Bubble net feeding can be divided into two categories based on the number of participating whales. The 􀀂rst category is humpback whale gathers in small groups to forage by the aid of \"bubble curtain wall\". The procedure is that whales continuously release bubbles around the prey. The bubbles form a precise curtain wall without any loopholes. Subsequently, a whale swims out from below of the 􀀂sh swarm and gobble them down. The whale that is already full will take over his teammate to release bubbles, and teammates will take turns to prey. The other category is that a single whale utilizes a \"spiral bubble net\" to capture small 􀀂sh and shrimp.  \nManuscript received November 15, 2024; revised May 23, 2025 .  \nThis work was supported in part by the National Natural Science Foundation of China (62166031), the research fund for the doctoral program of Ningbo University of Finance and Economics.  \nKui Liu is an associate professor of College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo, 315175, China. (corresponding author, email:liukui [1980@163.com](1980@163.com)).  \nYuguang Wang is an associate professor of Mathematics and Statistics Department, Ninxia University, Yinchuan, 750021, China. (email: [wangyuguang@nxu.edu](wangyuguang@nxu.edu)).  \nBy simulating the foraging behavior of humpback whales, Mirjalili et.al proposed a whale optimization algorithm[5](WOA) . During the past years, WOA has demonstrated signi􀀂cant success in solving various types of optimization problems characterized by non-convex, discontinuous and soon. However, similar to other meta-heuristic optimization algorithms, WOA also encounters several challenging issues. For instance, the lack of the balance between the exploration and the exploitation, they tend to struggle with poor convergence when solving complex multimodal function problems. These limitations have restricted the practical applications of W OA. A number of variant WOA algorithms have, hence, been proposed to solve these questions. These improvements can be categorized into two categories: 1) Introduce new","cbCaiqGVlUXHJ9Bp","https://ap.wps.com/l/cbCaiqGVlUXHJ9Bp","pdf",799878,1,6,"English","en",105,"# Introduction\n## Inspiration from humpback whale foraging\n## Bubble net feeding strategies\n## Related work and variants of WOA\n## Motivation and contributions of NWOA","[{\"question\":\"What problem does the proposed NWOA address compared with the whale optimization algorithm (WOA)?\",\"answer\":\"NWOA addresses WOA’s limitations, mainly the imbalance between exploration and exploitation and the risk of getting trapped in local optima.\"},{\"question\":\"How does the population diversity strategy improve NWOA?\",\"answer\":\"The population diversity strategy enhances the exploration property and helps maintain a better exploration–exploitation balance during the optimization process.\"},{\"question\":\"What do the numerical experiments indicate about NWOA performance?\",\"answer\":\"Numerical experiments show that NWOA is capable of producing higher quality solutions.\"}]","A Novel Whale Optimization Algorithm Based on Population Diversity Strategy | 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problem does the proposed NWOA address compared with the whale optimization algorithm (WOA)?","Question",{"text":76,"@type":77},"NWOA addresses WOA’s limitations, mainly the imbalance between exploration and exploitation and the risk of getting trapped in local optima.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the population diversity strategy improve NWOA?",{"text":81,"@type":77},"The population diversity strategy enhances the exploration property and helps maintain a better exploration–exploitation balance during the optimization process.",{"name":83,"@type":74,"acceptedAnswer":84},"What do the numerical experiments indicate about NWOA performance?",{"text":85,"@type":77},"Numerical experiments show that NWOA is capable of producing higher quality 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