A Micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategy

Qian, Shuqu; Ye, Yongqiang; Jiang, Bin; Xu, Guofeng

doi:10.1007/s00500-016-2370-0

Cited by 11 publications

(7 citation statements)

References 52 publications

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“…In most of existing MOIAs [53]- [55], the clonal selection strategy has been validated to speed up the convergence. More recently, a novel clonal selection approach, namely DCSS, based on the framework of decomposition is first proposed in MOIA-DCSS [56], which has been confirmed that it can enhance the ability of algorithms for solving some complicated MOPs.…”

Section: ) Decomposition Based Clonal Selectionmentioning

confidence: 99%

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

Chen

Gong

et al. 2019

IEEE Access

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Recently, there are a number of particle swarm optimization algorithms (PSOs) proposed for tackling multi-objective optimization problems (MOPs). Most of multi-objective PSOs (MOPSOs) were designed to speed up their convergence, which have been validated when tackling various kinds of MOPs. However, they may face some challenges for tackling some complicated MOPs, such as the UF test problems with complicated Pareto-optimal sets, mainly due to their neglect on the diversity. To solve the above problem, a novel hybrid MOPSO (called HMOPSO-ARA) is suggested in this paper with an adaptive resource allocation strategy, which shows a superior performance over most MOPSOs. Using the decomposition approach in HMOPSO-ARA, MOPs are transferred into a set of subproblems, each of which is accordingly optimized by one particle using a novel velocity update approach with the strengthened search capability. Then, an adaptive resource allocation strategy is employed based on the relevant improvement on the aggregated function, which can reasonably assign the computational resource to the particles according to their performance, so as to accelerate the convergence speed to the true Pareto-optimal front. Moreover, a decomposition-based clonal selection strategy is further used to enhance our performance, where the cloning process is run on the external archive based on the relevant fitness improvement. The experiments validate the superiority of HMOPSO-ARA over four competitive MOPSOs (SMPSO, CMPSO, dMOPSO and AgMOPSO) and four competitive multi-objective evolutionary algorithms (MOEA/D-ARA, MOEA/D-DE MOEA/D-GRA and EF_PD) when tackling thirty-five test problems (DTLZ1-DTLZ9, WFG1-WFG9, UF1-UF10 and F1-F9), in terms of two widely used performance indicators.

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Section: ) Decomposition Based Clonal Selectionmentioning

confidence: 99%

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

Chen

Gong

et al. 2019

IEEE Access

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“…The Hypervolume Difference(HVD) [22,41,61] measures the gap between the hypervolume of the obtained PF and that of the true PF. let P F t be a set of uniformly distributed Pareto optimal points of the PF in t time and let P t be an approximation set of PF in t time.…”

Section: Performance Indicatorsmentioning

confidence: 99%

“…(1) randomly initialize strategy (RIS), which randomly generates individuals in the feasible region of the decision space; (2) feed-forward prediction strategy(FPS) [38]; (3) population prediction strategy(PPS) [39]; (4) A micro-cloning dynamic multiobjective algorithm (mcD-MOA), which is with an adaptive change reaction strategy [41].…”

Section: Parameter Settingsmentioning

confidence: 99%

“…The rate of selecting is set to be α = 0.06. The monitoring threshold η is set to 0.01 and integer p = 20 [41]. Parameters in SPPS: In the process of predicting the non-dominated set by feed-forward center points, set the Gaussian perturbation d to be 0.1.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…In 2016, Muruganantham and Tan et al proposed a prediction method via Kalman Filter which can track PF accurately [32]. In 2017, Qian and Ye et al combined immune system and evolution system to propose a micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategy [41]. The idea of the algorithm is innovative and the effect is good for DMOPs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A predictive strategy based on special points for evolutionary dynamic multi-objective optimization

Zou

Yang

et al. 2018

Soft Comput

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There are some real-world problems in which multiple objectives conflict with each other and the objectives change with time. These problems require an optimization algorithm to track the moving Pareto front or Pareto set over time. In this paper, we propose a predictive strategy based on special points (SPPS) which consists of three mechanisms. The first one is that the non-dominated set is predicted directly by feed-forward center points, which can eliminate many useless individuals predicted by traditional prediction using feed-forward center points. The second one is that a special point set(such as boundary point, knee point, etc.) is introduced into the predicted population which can track Pareto front or Pareto set more accurately. The third one is the adaptive diversity maintenance mechanism based on boundary points and center points. The mechanism can introduce diverse individuals of the corresponding number according to the degree of difficulty of the problem to keep the diversity of the population. The number of these diverse individuals is strongly related to the center points. Then, they are generated evenly throughout the decision space between the boundary points. The proposed strategy is

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A novel multi-objective immune algorithm with a decomposition-based clonal selection

Lin

Liu

et al. 2019

Applied Soft Computing

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A Micro-cloning dynamic multiobjective algorithm with an adaptive change reaction strategy

Cited by 11 publications

References 52 publications

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

A predictive strategy based on special points for evolutionary dynamic multi-objective optimization

A novel multi-objective immune algorithm with a decomposition-based clonal selection

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