Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Many-Objective Optimization by Space Partitioning and Adaptive ε-Ranking on MNK-Landscapes
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
A preference-based evolutionary algorithm for multi-objective optimization
Evolutionary Computation
Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A population-based local search for solving a bi-objective vehicle routing problem
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
Controlling dominance area of solutions and its impact on the performance of MOEAs
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Objective space partitioning using conflict information for many-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Path relinking on many-objective NK-landscapes
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Adaptive objective space partitioning using conflict information for many-objective optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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Multi-objective random one-bit climbers moRBCs are one class of stochastic local search-based algorithms that maintain a reference population of solutions to guide their search. They have been shown to perform well in solving multi-objective optimization problems. In this work, we analyze the effects of introducing a list of tabu moves in the performance of moRBCs and investigate how moRBCs behave varying the size of this list. We also study their behavior when the selection to update the reference population and archive is replaced with a procedure that provides an alternative mechanism for preserving better distribution among the solutions. We use several MNK-landscape models as test instances and apply statistical testings to analyze the results. Our study shows that the two modifications complement each other in significantly improving moRBCs' performance especially in many-objective problems. They can play specific roles in enhancing the convergence and diversity of moRBCs. Moreover, they help improve the rate by which moRBCs can find solutions that have desired qualities.