{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:37:18Z","timestamp":1769711838611,"version":"3.49.0"},"reference-count":48,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Optimization problems in the field of industrial engineering usually involve massive amounts of information and complex scheduling process with the characteristics of high-dimension and non-convexity, which bring many challenges to finding an optimal solution. We proposed an improved beetle swarm optimization (IBSO) algorithm demonstrating the potential to solve different problems of path planning in static environment with good performance. Firstly, the algorithm is an upgrade of the original beetle antennae search (BAS) algorithm and the search strategy is improved by replacing a single beetle by multiple beetles. Secondly, the global search ability gets enhanced, and the diversity of optimization is improved through introducing nonlinear sinusoidal disturbance with Levy flight mechanism in beetles\u2019 position. Finally, the search performance of beetle swarm is improved by simulating the characteristics of employment bees to search for a better solution near the honey source field in the Artificial Bee Colony (ABC) algorithm. Our experiment results show that IBSO algorithm can achieve higher search efficiency and wider search ranges through well balancing the advantages of local search and fast optimization of the BAS algorithm with the global search of the improved mechanism. The IBSO algorithm has shown the potential to provide a new solution for several optimization problems in path planning in static environment.<\/jats:p>","DOI":"10.3233\/jifs-224163","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T11:42:23Z","timestamp":1696333343000},"page":"11453-11479","source":"Crossref","is-referenced-by-count":0,"title":["A mobile robot path planning using improved beetle swarm optimization algorithm in static environment"],"prefix":"10.1177","volume":"45","author":[{"given":"Yucheng","family":"Lyu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Guangxi Minzu University, Nanning, China"}]},{"given":"Yuanbin","family":"Mo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guangxi Minzu University, Nanning, China"},{"name":"Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, China"}]},{"given":"Songqing","family":"Yue","sequence":"additional","affiliation":[{"name":"Deparment of Computer Science and Software Engineering, University of Wisconsin-Platteville, Platteville, WI, USA"}]},{"given":"Lila","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guangxi Minzu University, Nanning, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-224163_ref1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.comnet.2017.05.021","article-title":"Wireless Sensor Networks and Multi-UAV systems for natural disaster management","volume":"124","author":"Milan Erdelj","year":"2017","journal-title":"Computer Networks"},{"issue":"17","key":"10.3233\/JIFS-224163_ref2","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1080\/01691864.2013.838333","article-title":"Real-time exploration of a multi-robot rescue system in disaster 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