In order to solve the problems of the traditional whale optimization algorithm, such as slow convergence speed, low optimization precision and easy to fall into the local optimal solution, an improved algorithm combining elite disturbance opposition-based learning and dynamic spiral updating (OWOA) was proposed. Firstly, the whale population is initialized by opposition-based learning strategies to ensure the diversity of the population , and then elite whales are multiple chaos disturbed to avoid falling into local optimal solution; Secondly, the algorithm uses a dynamic spiral updating strategy, and dynamically adjusts the spiral shape with the iteration times, thus improving the optimization accuracy of the algorithm. Finally, using 12 classic reference functions, CEC2014 test set and CEC2017 test set to evaluate the effectiveness of OWOA. In addition, optimum power flow(OPF) is employed for estimating the efficacy of the OWOA in practical applications. The experimental results show that:compared with other algorithms, the algorithm in this paper has higher convergence speed and accuracy in unimodal function, multi-peak function and multi-dimensional function, and which is more competitive in providing optimal solutions for optimization problems.
Aiming at the problems of uneven distribution of initialized populations and unbalanced exploration and exploitation leading to slow convergence, low convergence accuracy, and easy to fall into local optimality of marine predators algorithm (MPA), a marine predators algorithm based on adaptive weight and chaos factor is proposed (ACMPA), the algorithm is applied to the traveling salesman problem (TSP), and the shortest path planning and research are carried out for the traveling salesman problem. Firstly, the improved adaptive weight strategy is used to balance the exploration and exploitation stage of the algorithm and improve the convergence accuracy of the algorithm. Secondly, the chaos factor is used to replace the random factor, and the ergodicity of the chaos factor is used to make it easier for predators to jump out of local optimization and enhance the optimization ability of the algorithm. Finally, 10 benchmark test functions, the CEC2015 test set, and the CEC2017 test set are used to evaluate the effectiveness of the ACMPA. The results show that, compared with the other four intelligent optimization algorithms, the improved ACMPA achieves better results in both mean and standard deviation, and the algorithm has a better effect on the shortest path problem.
In order to solve the problems of the traditional whale optimization algorithm, such as slow convergence speed, low optimization precision and easy to fall into the local optimal solution, an improved algorithm combining elite disturbance opposition-based learning and dynamic spiral updating (OWOA) was proposed. Firstly, the whale population is initialized by opposition-based learning strategies to ensure the diversity of the population, and then elite whales are multiple chaos disturbed to avoid falling into local optimal solution; Secondly, the algorithm uses a dynamic spiral updating strategy, and dynamically adjusts the spiral shape with the iteration times, thus improving the optimization accuracy of the algorithm. Finally, using 12 classic reference functions, CEC2014 test set and CEC2017 test set to evaluate the effectiveness of OWOA. In addition, optimum power flow(OPF) is employed for estimating the efficacy of the OWOA in practical applications. The experimental results show that:compared with other algorithms, the algorithm in this paper has higher convergence speed and accuracy in unimodal function, multi-peak function and multi-dimensional function, and which is more competitive in providing optimal solutions for optimization problems.
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