Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
Island Model Cooperating with Speciation for Multimodal Optimization
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method
Selected Papers from AISB Workshop on Evolutionary Computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Efficient differential evolution using speciation for multimodal function optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Niching in evolution strategies
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Innovization: innovating design principles through optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Constrained optimization using multiple objective programming
Journal of Global Optimization
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Multiobjective Problem Solving from Nature: From Concepts to Applications (Natural Computing Series)
Multiobjective Problem Solving from Nature: From Concepts to Applications (Natural Computing Series)
Multiobjectivization by Decomposition of Scalar Cost Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Generator for Multimodal Test Functions with Multiple Global Optima
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Evolutionary algorithms and multi-objectivization for the travelling salesman problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Guiding single-objective optimization using multi-objective methods
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A bi-criterion approach to multimodal optimization: self-adaptive approach
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Niche radius adaptation in the CMA-ES niching algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A sequential niching technique for particle swarm optimization
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
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In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper, we have solved up to 16-variable test problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application.