Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Generalized homotopy approach to multiobjective optimization
Journal of Optimization Theory and Applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing)
Mesh Adaptive Direct Search Algorithms for Constrained Optimization
SIAM Journal on Optimization
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multiobjective Optimization Through a Series of Single-Objective Formulations
SIAM Journal on Optimization
HCS: a new local search strategy for memetic multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Handbook of Multicriteria Analysis
Handbook of Multicriteria Analysis
Winter Simulation Conference
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Use of retrospective optimization for placement of oil wells under uncertainty
Proceedings of the Winter Simulation Conference
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A new method is proposed using a gradient-based zigzag search approach for multiobjective optimization MOO or vector optimization problems. The key idea of this method is searching around the Pareto front by applying an efficient local search procedure using the gradients of the objective functions. This local search zigzags along the Pareto surface guided by the gradients and iteratively returns the visited Pareto optima. Many continuous MOO problems have smooth objective functions and the set of the nondominated objective function values forms a regular surface in the image space. This fact motivates developing the zigzag search method for such relatively well-posed MOO problems. A simple implementation of this method, z-algorithm, is presented particularly for continuous bi-objective optimization BOO problems with well-connected Pareto optimal solutions. The efficiency of the z-algorithm is studied with a set of BOO problems and the algorithm performances are compared to those of a recently developed MOO algorithm, Pareto front approximation with adaptive weighted sum method.