Adaptive global optimization with local search
Adaptive global optimization with local search
Memetic algorithms: a short introduction
New ideas in optimization
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
A genetic algorithm for a bi-objective capacitated arc routing problem
Computers and Operations Research
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
How to choose solutions for local search in multiobjective combinatorial memetic algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Network topology planning using MOEA/D with objective-guided operators
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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This paper proposes an idea of using heuristic local search procedures specific for single-objective optimization in multiobjective genetic local search (MOGLS). A large number of local search techniques have been studied for various combinatorial optimization problems. Thus we may have a situation where a powerful local search procedure specific for a particular objective is available in multiobjective optimization. Such a local search procedure, however, can improve only a single objective. Moreover, it may have severe side-effects on the other objectives. For example, in a scheduling problem, an insertion move of a job with the maximum delay to an earlier position in a current schedule is likely to improve only the maximum tardiness. In this paper, we assume a situation where each objective has its own heuristic local search procedure. First we explain our MOGLS algorithm, which is the hybridization of NSGA-II and weighted sum-based local search. Next we propose an idea of using heuristic local search procedures specific for single-objective optimization in MOGLS. Then we implement the proposed idea as a number of variants of MOGLS. These variants are different from each other in the choice of a heuristic local search procedure. We examine three schemes: random, probabilistic and deterministic. Finally we examine the performance of each variant through computational experiments on multiobjective 0/1 knapsack problems with two, three and four objectives. It is shown that the use of heuristic local search procedures and their appropriate choice improve the performance of MOGLS.