Octopus Inspired Optimization Algorithm: Multi-Level Structures and Parallel Computing Strategies

• URL: http://arxiv.org/abs/2410.07968v2
• Date: Fri, 17 Jan 2025 08:55:50 GMT
• Title: Octopus Inspired Optimization Algorithm: Multi-Level Structures and Parallel Computing Strategies
• Authors: Xu Wang, Longji Xu, Yiquan Wang, Yuhua Dong, Xiang Li, Jia Deng, Rui He,
• Abstract summary: Octopus Inspired Optimization (OIO) algorithm is inspired by the neural structure of octopus, especially its hierarchical and decentralised interaction properties.<n>OIO shows faster convergence and higher accuracy, especially when dealing with multimodal functions and high-dimensional optimisation problems.<n>It is especially suitable for application scenarios that require fast, efficient and robust optimisation methods, such as robot path planning, supply chain management, and energy system management.
• Score: 21.96416191573034
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper introduces a novel bionic intelligent optimisation algorithm, Octopus Inspired Optimization (OIO) algorithm, which is inspired by the neural structure of octopus, especially its hierarchical and decentralised interaction properties. By simulating the sensory, decision-making, and executive abilities of octopuses, the OIO algorithm adopts a multi-level hierarchical strategy, including tentacles, suckers, individuals and groups, to achieve an effective combination of global and local search. This hierarchical design not only enhances the flexibility and efficiency of the algorithm, but also significantly improves its search efficiency and adaptability. In performance evaluations, including comparisons with existing mainstream intelligent optimisation algorithms, OIO shows faster convergence and higher accuracy, especially when dealing with multimodal functions and high-dimensional optimisation problems. This advantage is even more pronounced as the required minimum accuracy is higher, with the OIO algorithm showing an average speedup of 2.27 times that of conventional particle swarm optimisation (PSO) and 9.63 times that of differential evolution (DE) on multimodal functions. In particular, when dealing with high-dimensional optimisation problems, OIO achieves an average speed of 10.39 times that of DE, demonstrating its superior computational efficiency. In addition, the OIO algorithm also shows a reduction of about 5\% in CPU usage efficiency compared to PSO, which is reflected in the efficiency of CPU resource usage also shows its efficiency. These features make the OIO algorithm show great potential in complex optimisation problems, and it is especially suitable for application scenarios that require fast, efficient and robust optimisation methods, such as robot path planning, supply chain management optimisation, and energy system management.

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