A predictable artificial physics optimisation algorithm

Xie, Liping; Zeng, Jianchao; Yang, Qiongqiong

doi:10.1504/ijcsm.2015.072966

Cited by 3 publications

(2 citation statements)

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“…The performance of IFNS-MOAPO algorithm and other comparison algorithms are compared through simulation experiments. The comparison algorithms in this paper are NSGA-II [29], CMOPSO [30], MOAPO [25], MOEA/D-DAE [31], CCMO [32], respectively. The reasons for choosing these comparison algorithms are as follows: (i) NSGA-II is an algorithm based on non-dominated sorting, and it is used as a comparison to test the effectiveness of the improved nondominated sorting method in the IFNS-MOAPO algorithm to save time costs.…”

Section: A Experimental Environment Configuration and Benchmark Test ...mentioning

confidence: 99%

“…To address the shortcomings of the traditional APO algorithm with low efficiency of non-dominated solution selection, an improved fast non-dominated sorting strategy is introduced to save the time cost of the algorithm. In the iterative solution of the traditional APO algorithm, it is necessary to first synthesize multiple objectives into one objective, and then find the optimal individual and the worst individual in the population before the final calculation of individual quality, which does not fully reflect the concept of Pareto domination and cannot fully reflect the characteristics of multi-objective optimization [25,26]. This paper reconstructs the quality function based on individual partitioning and establishes a direct mapping between the calculation of individual quality and individual ordinal values and constraint violation degrees, which fully reflects the characteristics of multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Fan

et al. 2022

IEEE Access

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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 partitionguided individual evolution into the APO algorithm to overcome this imperfection. An improved multiobjective 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, seven 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).

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