Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multicriteria Optimization
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
Global search perspectives for multiobjective optimization
Journal of Global Optimization
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In this paper, a symbolic algorithm for solving constrained multi-objective optimisation problems is proposed. It is used to get the Pareto optimal solutions as functions of KKT multipliers $\overrightarrow{\lambda}$ for multi-objective problems with continuous, differentiable, and convex/pseudo-convex functions. The algorithm is able to detect the relationship between the decision variables that form the exact curve/hyper-surface of the Pareto front. This algorithm enables to formulate an analytical form for the true Pareto front which is necessary in absolute performance measurement of evolutionary computing techniques. Here the proposed technique is tested on some test problems which have been chosen from a number of significant past studies. The results show that the proposed symbolic algorithm is robust to find the analytical formula of the exact Pareto front.