An Improved Multi-Objective Artificial Physics Optimization Algorithm Based on Multi-Strategy Fusion

The elements of the population of the artificial physics optimization (APO) algorithm are assigned mass, velocity, and displacement attributes. It is a new type of heuristic algorithm, but it still has defects such as low efficiency of non-dominated solution selection and unbalanced search capacity...

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Bibliographic Details
Published inIEEE access Vol. 10; pp. 108736 - 108748
Main Authors Sun, Bao, Zhang, Lijing, Li, Zhanlong, Fan, Kai, Jin, Qinqin, Guo, Jin
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial physics optimization
Chemical partition
Convergence
Evolution
Heuristic algorithms
Heuristic methods
mass function
Mathematical analysis
multi-objective optimization
Multiple objective analysis
Optimization
Optimization algorithms
Pareto optimization
partition evolution
Partitioning algorithms
Physics
Runtime
Social factors
Sorting
Sorting algorithms
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Summary:The elements of the population of the artificial physics optimization (APO) algorithm are assigned mass, velocity, and displacement attributes. It is a new type of heuristic algorithm, but it still has defects such as low efficiency of non-dominated solution selection and unbalanced search capacity of global and local. This paper introduces the mechanism of improved fast non-dominant sorting and partition-guided individual evolution into the APO algorithm to overcome this imperfection. An improved multi-objective artificial physics optimization algorithm based on fast non-dominated sorting (IFNS-MOAPO) is proposed, which is the result of integrating numerous strategies into the APO algorithm. Firstly, an improved fast non-dominated sorting strategy is introduced. This strategy can increase the efficiency of selecting non-dominated solutions and decrease the running time of the algorithm. Secondly, the mechanism of individual evolution guided by partition is proposed. For individuals in infeasible and feasible domains, different mass functions and virtual force calculation rules are adopted to update the algorithm iteratively to boost the convergence performance. To verify the comprehensive performance of the IFNS-MOAPO algorithm, eleven benchmark test problems are selected for simulation experiments and compared with five algorithms in terms of runtime duration, Pareto front plots, and metric values. The results show that the IFNS-MOAPO algorithm has good distribution and can converge to the true Pareto front quickly. It is a useful tool for solving constrained multi-objective optimization problems (CMOPs).
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3210984