Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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The aims of multi-objective optimization are (1) to find pareto-optimal solutions and (2) to analyze the trade-off between conflicting objectives. This paper proposes an interactive method for solving multi-objective optimization problems using the satisficing trade-off method (STOM). In particular, we introduce a trade-off matrix to quantitatively analyze the trade-off between conflicting objectives. Each element of the trade-off matrix consists of a projection matrix of active constraints and gradients of objective functions. In addition, the compromise point and the compromise solution incorporating the trade-off ratio that the decision-maker desires are defined in this paper. The technique to obtain the compromise point is proposed in this paper. Through numerical examples, the validity proposed method is examined.