The Traveling Salesman Problem (TSP) is a combinatorial optimization problem widely used to test new heuristics. bee-inspired algorithms are receiving great attention from the Swarm Intelligence field due to their cap...
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ISBN:
(纸本)9781467389853
The Traveling Salesman Problem (TSP) is a combinatorial optimization problem widely used to test new heuristics. bee-inspired algorithms are receiving great attention from the Swarm Intelligence field due to their capability of providing good solutions in reasonable time to complex problems. This paper takes the optbees, a bee-inspired algorithm used for continuous optimization, and proposes the necessary modifications to solve the TSP, generating the TSPoptbees. The proposed algorithm is evaluated using benchmark instances and the results are compared to other similar works from the literature.
This paper introduces a new variant of the bees algorithm (BA) called bees algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisat...
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This paper introduces a new variant of the bees algorithm (BA) called bees algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA's parameters by combining exploration and exploitation strategies while preserving the algorithm's core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm's core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems.
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