2021
DOI: 10.1109/jas.2021.1004129
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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Abstract: Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most repr… Show more

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Cited by 494 publications
(228 citation statements)
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References 114 publications
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“…In this paper, we study the recommendation algorithm that fits the business scenario, how the recommendation engine works. The optimization direction of the algorithm is given in conjunction with the actual problem [ 5 ]. The idea of solving the problem of cold start and user interest prediction in the music recommendation scenario is given, which is a guideline for the implementation in recommendation system engineering applications.…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, we study the recommendation algorithm that fits the business scenario, how the recommendation engine works. The optimization direction of the algorithm is given in conjunction with the actual problem [ 5 ]. The idea of solving the problem of cold start and user interest prediction in the music recommendation scenario is given, which is a guideline for the implementation in recommendation system engineering applications.…”
Section: Introductionmentioning
confidence: 99%
“…Heuristic algorithms are a subset of the artificial intelligence field, which is popular in solving different optimization problems and is often used to solve task scheduling problems [28,29]. Common heuristic algorithms include ant colony optimization (ACO) [30], genetic algorithm (GA) [31], particle swarm optimization (PSO) [32], simulated annealing algorithm (SAA) [33], Grey Wolf Optimizer (GWO) [34], monarch butterfly optimization algorithm (MBO) [35], and so on.…”
Section: Heuristic Algorithm To Solve the Task Scheduling Problemmentioning
confidence: 99%
“…The ACO can search on a large scale. To improve its search process, it can have excellent exploration and development capabilities at the stage of generating the optimal solution [28].…”
Section: Heuristic Algorithm To Solve the Task Scheduling Problemmentioning
confidence: 99%
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“…In this case, they are at great risk of falling into local areas, especially when tackling multimodal problems with many wide and flat local regions, whereas heuristic algorithms could search the solution space in different directions by maintaining a population of individual solutions. (3) Heuristic algorithms usually preserve inherent parallelism to accelerate the iteration [18,22]. Specifically, at least, during the optimization, the fitness evaluation of each individual solution, which is usually the most time-consuming part in heuristic algorithms, could be separately executed, let alone that some parallel techniques could be designed and embedded into heuristic algorithms to accelerate the search process.…”
Section: Introductionmentioning
confidence: 99%