Abstract
Many-objective optimization problems (MaOPs) refer to those multi-objective problems (MOPs) withmore than three objectives. In order to solve MaOPs, a multi-objective particle swarm optimization algorithm based on new fitness assignment and multi cooperation strategy(FAMSHMPSO) is proposed. Firstly, this paper proposes a new fitness allocation method based on fuzzy information theory to enhance the convergence of the algorithm. Then a new multi criteria mutation strategy is introduced to disturb the population and improve the diversity of the algorithm. Finally, the external files are maintained by the three-point shortest path method, which improves the quality of the solution. The performance of FAMSHMPSO algorithm is evaluated by evaluating the mean value, standard deviation and IGD+ index of the target value on dtlz test function set of different targets of FAMSHMPSO algorithm and other five representative multi-objective evolutionary algorithms. The experimental results show that FAMSHMPSO algorithm has obvious performance advantages in convergence, diversity and robustness.
Highlights
In today’s scientific research and engineering practice, the problems faced by decision makers are becoming more and more complex, and often need to deal with multiple objectives at the same time
The performance of FAMSHMPSO algorithm is evaluated by evaluating the mean value, standard deviation, and Inverted Generational Distance (IGD)+ index of the target value on dtlz test function set of different targets of FAMSHMPSO algorithm and other five representative multi-objective evolutionary algorithms
Experiment Setup 3.1.1 Peer Comparison Algorithm In order to test the performance of FAMSHMPSO algorithm, five algorithms such as PSO classical algorithm OMOPSO, PSO improved algorithm SMPSO, decomposition based D2MOPSO, improved non-inferior classification genetic algorithm NSGA-III and file-based hybrid decentralized search algorithm AbYSS are selected as peer-to-peer comparison algorithms
Summary
In today’s scientific research and engineering practice, the problems faced by decision makers are becoming more and more complex, and often need to deal with multiple objectives at the same time. For example: Kumar et al(2016) proposed a self-learning MOPSO algorithm to deal with multi-objective optimization problems This algorithm proposed 4 kinds of calculation methods on particle speed and position update, which effectively improved the effectiveness of each particle search. Based on the existing PSO algorithm and its variants, this paper proposes a new high-dimensional particle swarm evolution algorithm based on the characteristics of many-objective optimization problems This algorithm is different from the existing high-dimensional particle swarm evolution algorithms in that: 1) Combined with fuzzy information theory, a fitness allocation method based on fuzzy correlation entropy is proposed, which objectively allocates fitness values, which increases the pressure of population selection, eliminates the influence of external uncertain factors on the algorithm, simplifies the algorithm process, effectively avoids the disadvantage of insufficient population selection pressure caused by traditional fitness allocation, and makes the algorithm having better convergence.
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