Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A preference-based evolutionary algorithm for multi-objective optimization
Evolutionary Computation
Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Interactive MOEA/D for multi-objective decision making
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
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Real-world optimization involving multiple objectives in changing environment known as dynamic multi-objective optimization (DMO) is a challenging task, especially special regions are preferred by decision maker (DM). Based on a novel preference dominance concept called sphere-dominance and the theory of artificial immune system (AIS), a sphere-dominance preference immune-inspired algorithm (SPIA) is proposed for DMO in this paper. The main contributions of SPIA are its preference mechanism and its sampling study, which are based on the novel sphere-dominance and probability statistics, respectively. Besides, SPIA introduces two hypermutation strategies based on history information and Gaussian mutation, respectively. In each generation, which way to do hypermutation is automatically determined by a sampling study for accelerating the search process. Furthermore, The interactive scheme of SPIA enables DM to include his/her preference without modifying the main structure of the algorithm. The results show that SPIA can obtain a well distributed solution set efficiently converging into the DM's preferred region for DMO.