A hybrid many‐objective optimization algorithm for coal green production problem

Cui, Zhihua; Zhang, Jiangjiang

doi:10.1002/cpe.6040

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…However, the crowding selection mechanism has been proved that the selection pressure will drop in the later stage of the algorithm, which lead to the solution cannot be screened well. 24,49 To overcome this problem, a comprehensive fitness evaluation mechanism is used to replace the original crowding selection mechanism. Specific principles will be presented in the following sections.…”

Section: Design Algorithmmentioning

confidence: 99%

“…And cloning, recombination, and mutation only apply to the selected individuals (active antibodies). However, the crowding selection mechanism has been proved that the selection pressure will drop in the later stage of the algorithm, which lead to the solution cannot be screened well 24,49 . To overcome this problem, a comprehensive fitness evaluation mechanism is used to replace the original crowding selection mechanism.…”

Section: Design Algorithmmentioning

confidence: 99%

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A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

Ye¹,

Li²,

Zhang

et al. 2022

Concurrency and Computation

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Summary Data imbalance and parameter selection have always been two important factors affecting the accuracy of software defect prediction. However, the existing methods have been poor in balancing these two factors. To address this challenge, a multi‐objective software defect prediction model is employed to describe the under sampled software defect prediction problem. And in this model, defect detection rate and defect false alarm rate are deemed as two objectives which should be optimized. Simultaneously, we design a novel multi‐objective immune optimization algorithm based on the comprehensive fitness of evaluation mechanism to effectively address the employed model. In the algorithm, the original mechanism based on neighborhood individual selection is replaced based on comprehensive fitness evaluation,which have better selection ability to attaining the predicted effect of software to improving in the process of selection solution of population evolution and further effectively help decision makers choose better scheme that meets requirements. In addition, in order to verify the effectiveness of the designed algorithm, the proposed algorithm is compared on eight different public data sets. Simulation results show that the proposed algorithm has better performance in handling with the multi‐objective under sampling software defect prediction problem.

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Section: Design Algorithmmentioning

confidence: 99%

Section: Design Algorithmmentioning

confidence: 99%

A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

Ye¹,

Li²,

Zhang

et al. 2022

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

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An enhancing many-objective evolutionary algorithm using chaotic mapping and solution ranking mechanism for large-scale optimization

Wang

Zhang

Wang

et al. 2022

Journal of Computational Design and Engineering

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There are many complex optimization problems in the real world, and various evolutionary algorithms are proposed to solve them. Recently, the many-objective evolutionary algorithm using a one-by-one selection strategy (1by1EA) adopts a convergence indicator and a distribution indicator to balance convergence and diversity. However, the algorithm is too random in initialization and the fitness evaluation of solutions in the mating selection is single, which leads to poor performance in solving large-scale problems. Therefore, this paper proposes an improved method called 1by1EA-CHV by using Circle chaotic mapping and a solution ranking mechanism based on the hypervolume (HV) indicator. We first map each component of solutions into a certain value space to initialize the population. Then, we calculate the contribution of each partition divided based on HV and apply the aggregation method to guide the reallocation of fitness, which achieves the ranking of solutions by using it before the old calculation method. To validate the performance, experiments compared 1by1EA-CHV with 1by1EA and other seven many-objective algorithms on large-scale functions, and the differences between these algorithms were analyzed statistically by a non-parametric test. The results showed the superiority of 1by1EA-CHV in solving large-scale many-objective optimization problems with up to 2000 decision variables.

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Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

Bai

Fan

et al. 2022

Complex Syst. Model. Simul.

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The trajectory planning of multiple unmanned aerial vehicles (UAVs) is the core of efficient UAV mission execution. Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods. However, multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment. Therefore, a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance, trajectory time, trajectory threat, and trajectory coordination distance costs of UAVs. The NSGA-III algorithm, which overcomes the problems of traditional trajectory planning, is used to solve the model. This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm. Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm, thereby addressing different actual needs.

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A hybrid many‐objective optimization algorithm for coal green production problem

Cited by 3 publications

References 38 publications

A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

An enhancing many-objective evolutionary algorithm using chaotic mapping and solution ranking mechanism for large-scale optimization

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

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