Numerical continuation methods: an introduction
Numerical continuation methods: an introduction
Adaptive global optimization with local search
Adaptive global optimization with local search
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Tabu Search
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Stochastic method for the solution of unconstrained vector optimization problems
Journal of Optimization Theory and Applications
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Hybrid multiobjective genetic algorithm with a new adaptive local search process
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Convergence of stochastic search algorithms to gap-free pareto front approximations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new memetic strategy for the numerical treatment of multi-objective optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A hybrid multiagent approach for global trajectory optimization
Journal of Global Optimization
Evolutionary continuation methods for optimization problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Covering pareto sets by multilevel evolutionary subdivision techniques
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Generalization of dominance relation-based replacement rules for memetic EMO algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
About selecting the personal best in multi-objective particle swarm optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Searching for overlapping coalitions in multiple virtual organizations
Information Sciences: an International Journal
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
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
Multi-objective zone mapping in large-scale distributed virtual environments
Journal of Network and Computer Applications
High-dimensional objective optimizer: An evolutionary algorithm and its nonlinear analysis
Expert Systems with Applications: An International Journal
Multi-objective immune algorithm with Baldwinian learning
Applied Soft Computing
Using computational intelligence for large scale air route networks design
Applied Soft Computing
A local multiobjective optimization algorithm using neighborhood field
Structural and Multidisciplinary Optimization
A co-evolutionary multi-objective optimization algorithm based on direction vectors
Information Sciences: an International Journal
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
Multi Agent Collaborative Search based on Tchebycheff decomposition
Computational Optimization and Applications
Zigzag Search for Continuous Multiobjective Optimization
INFORMS Journal on Computing
QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules
Information Sciences: an International Journal
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In this paper, we propose and investigate a new local search strategy for multiobjective memetic algorithms. More precisely, we suggest a novel iterative search procedure, known as the Hill Climber with Sidestep (HCS), which is designed for the treatment of multiobjective optimization problems, and show further two possible ways to integrate the HCS into a given evolutionary strategy leading to new memetic (or hybrid) algorithms. The pecularity of the HCS is that it is intended to be capable both moving toward and along the (local) Pareto set depending on the distance of the current iterate toward this set. The local search procedure utilizes the geometry of the directional cones of such optimization problems and works with or without gradient information. Finally, we present some numerical results on some well-known benchmark problems, indicating the strength of the local search strategy as a standalone algorithm as well as its benefit when used within a MOEA. For the latter we use the state of the art algorithms Nondominated Sorting Genetic Algorithm-II and Strength Pareto Evolutionary Algorithm 2 as base MOEAs.