Generating multiple reference vectors for a class of many-objective optimization problems with degenerate Pareto fronts

Hua, Yicun; Jin, Yaochu; Hao, Kuangrong; Cao, Yuan

doi:10.1007/s40747-020-00136-5

Cited by 16 publications

(5 citation statements)

References 28 publications

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“…x jkm l jk . (7) We wish that all the districts can complete their work at the same time, so the expected end time of each district is ought to be as balanced as possible.…”

Section: Fig 4 Illustration Of Immunization Operationsmentioning

confidence: 99%

“…The multi-objective optimization algorithm (MOA) has been a hot issue in recent years because of its balance on each aspects of the problem, which mainly consists of the particle swarm optimization (PSO) algorithms [1][2][3], the immune clonal algorithms (ICA) [4], the evolutionary algorithms (EA) [5][6][7], the differential evolution (DE) algorithms [8,9], and other hybrid heuristic algorithms [10][11][12][13][14]. These biological heuristics algorithms obtain better solutions through a continuous iterative process,in which a set of search rules are proposed.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic immune cooperative scheduling of agricultural machineries

Liu

Zhu

Hao

2021

Complex Intell. Syst.

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Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

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“…x jkm l jk . (7) We wish that all the districts can complete their work at the same time, so the expected end time of each district is ought to be as balanced as possible.…”

Section: Fig 4 Illustration Of Immunization Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic immune cooperative scheduling of agricultural machineries

Liu

Zhu

Hao

2021

Complex Intell. Syst.

Self Cite

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“…By contrast, a bottom-up hierarchical clustering algorithm is adopted [30], which can more precisely identify the center of a cluster to generate a corresponding reference vector. In [31], piecewise mapping clustering is adopted for determining the location of the reference vectors, aiming to solve degenerate problems. In [32], both partitional clustering and hierarchical clustering are adopted and the similarity of the solutions is measured by the acute angle between them.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Reference Vector-Guided Evolutionary Algorithm Using Growing Neural Gas for Many-Objective Optimization of Irregular Problems

Liu

Jin

Heiderich

et al. 2022

IEEE Trans. Cybern.

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100

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Most reference vector based decomposition algorithms for solving multi-objective optimization problems may not be well suited for solving problems with irregular Pareto fronts because the distribution of predefined reference vectors may not match well with the distribution of the Pareto optimal solutions. Thus, adaptation of the reference vectors is an intuitive way for decomposition based algorithms to deal with irregular Pareto fronts. However, most existing methods frequently change the reference vectors based on the activeness of the reference vectors within specific generations, slowing down the convergence of the search process. To address this issue, we propose a new method to learn the distribution of the reference vectors using the growing neural gas network to achieve automatic yet stable adaptation. To this end, an improved growing neural gas is designed for learning the topology of the Pareto fronts with the solutions generated during a period of the search process as the training data. We use the individuals in the current population as well as those in previous generations to train the growing neural gas to strike a balance between exploration and exploitation. Comparative studies conducted on popular benchmark problems and a realworld hybrid vehicle controller design problem with complex and irregular Pareto fronts show that the proposed method is very competitive.

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“…There are many practical multi-objective optimization problems (MOOPs) in engineering and scientific research [21,38,47]. A number of multi-objective evolutionary algorithms (MOEAs) and swarm intelligence optimization algorithms (MOSIOAs) have been proposed to solve MOOPs for they can obtain a well-distributed and well-converged set of near Pareto-optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multi-population Harris Hawks optimization for many-objective optimization

Yang

Tang

Cai

et al. 2022

Complex Intell. Syst.

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This paper presents an efficient cooperative multi-populations swarm intelligence algorithm based on the Harris Hawks optimization (HHO) algorithm, named CMPMO-HHO, to solve multi-/many-objective optimization problems. Specifically, this paper firstly proposes a novel cooperative multi-populations framework with dual elite selection named CMPMO/des. With four excellent strategies, namely the one-to-one correspondence framework between the optimization objectives and the subpopulations, the global archive for information exchange and cooperation among subpopulations, the logistic chaotic single-dimensional perturbation strategy, and the dual elite selection mechanism based on the fast non-dominated sorting and the reference point-based approach, CMPMO/des achieves considerably high performance on solutions convergence and diversity. Thereafter, in each subpopulation, HHO is used as the single objective optimizer for its impressive high performance. Notably, however, the proposed CMPMO/des framework can work with any other single objective optimizer without modification. We comprehensively evaluated the performance of CMPMO-HHO on 34 multi-objective and 19 many-objective benchmark problems and extensively compared it with 13 state-of-the-art multi/many-objective optimization algorithms, three variants of CMPMO-HHO, and a CMPMO/des based many-objective genetic algorithm named CMPMO-GA. The results show that by taking the advantages of the CMPMO/des framework, CMPMO-HHO achieves promising performance in solving multi/many-objective optimization problems.

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Generating multiple reference vectors for a class of many-objective optimization problems with degenerate Pareto fronts

Cited by 16 publications

References 28 publications

Dynamic immune cooperative scheduling of agricultural machineries

Dynamic immune cooperative scheduling of agricultural machineries

An Adaptive Reference Vector-Guided Evolutionary Algorithm Using Growing Neural Gas for Many-Objective Optimization of Irregular Problems

Cooperative multi-population Harris Hawks optimization for many-objective optimization

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