Abstract
The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems.
Highlights
With the development of modern technology, artificial intelligence is becoming more and more important in society, and more and more mature, and can be used to deal with many problems that cannot be solved by traditional methods, such as wind energy decision system (WEDS) [1]and social cognitive radio network (SCRN) [2]
In this paper, aiming at the disadvantage of poor local search ability of krill herd (KH) algorithm, we optimized it by simulated annealing strategy and quantum particle swarm optimization algorithm (QPSO) algorithm, and proposed a new algorithm: annealing krill quantum particle swarm optimization (AKQPSO)
This algorithm was compared with eight other excellent algorithms, and the computational accuracy of AKQPSO was tested by the 100-Digit Challenge problem
Summary
With the development of modern technology, artificial intelligence is becoming more and more important in society, and more and more mature, and can be used to deal with many problems that cannot be solved by traditional methods, such as wind energy decision system (WEDS) [1]. In order to study an algorithm with very high accuracy, as well as to simultaneously optimize the exploitation and exploration and improve the accuracy of annealing krill quantum particle swarm optimization (AKQPSO), we studied the KH algorithm with strong exploitation and the QPSO algorithm with strong exploration By combining their advantages, the new algorithm overcomes their original shortcomings and has a strong ability in exploration and exploitation. The 100-Digit Challenge problem is difficult to solve by traditional methods, and we can solve this problem better by using a swarm intelligence algorithm. Traditional methods need a lot of computation to solve this challenge, and they cannot get good results, so we use swarm intelligence algorithm to solve these problems, and get satisfactory results.
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