Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Introduction to Algorithms
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Gradient Based Stochastic Mutation Operators in Evolutionary Multi-objective Optimization
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Adapting to the Habitat: On the Integration of Local Search into the Predator-Prey Model
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Using gradient-based information to deal with scalability in multi-objective evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On gradient based local search methods in unconstrained evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Constraint-handling method for multi-objective function optimization: Pareto descent repair operator
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Exploiting second order information in computational multi-objective evolutionary optimization
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Proceedings of the 12th annual conference on Genetic and evolutionary computation
New challenges for memetic algorithms on continuous multi-objective problems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Using gradient information for multi-objective problems in the evolutionary context
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolving policies for multi-reward partially observable markov decision processes (MR-POMDPs)
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Parallel predator---prey interaction for evolutionary multi-objective optimization
Natural Computing: an international journal
Elitist archiving for multi-objective evolutionary algorithms: to adapt or not to adapt
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Genetic Algorithm (GA) is known as a potent multiobjective optimization method, and the effectiveness of hybridizing it with local search (LS) has recently been reported in the literature. However, there is a relatively small number of studies on LS methods for multiobjective function optimization. Although each of the existing LS methods has some strong points, they have respective drawbacks such as high computational cost and inefficiency in improving objective functions. Hence, a more effective and efficient LS method is being sought, which can be used to enhance the performance of the hybridization.Defining Pareto descent directions as descent directions to which no other descent directions are superior in improving all objective functions, this paper proposes a new LS method, Pareto Descent Method (PDM), which finds Pareto descent directions and moves solutions in such directions thereby improving all objective functions simultaneously. In the case part or all of them are infeasible, it finds feasible Pareto descent directions or descent directions as appropriate. PDM finds these directions by solving linear programming problems, which is computationally inexpensive. Experiments have shown PDM's superiority over existing methods.