Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
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)
A coordination method for fuzzy multi-objective optimization of system reliability
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Multiobjective optimization using a Pareto differential evolution approach
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Evolutionary multiobjective optimization for the design of fuzzy rule-based ensemble classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
A symbiotic genetic algorithm with local-and-global mapping search for reinforcement fuzzy control
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization
Information Sciences: an International Journal
Grenade Explosion Method-A novel tool for optimization of multimodal functions
Applied Soft Computing
Genetic evolving ant direction HDE for OPF with non-smooth cost functions and statistical analysis
Expert Systems with Applications: An International Journal
GA-based solutions comparison for warehouse storage optimization
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Multi-objective optimization with artificial weed colonies
Information Sciences: an International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Engineering Applications of Artificial Intelligence
Causally-guided evolutionary optimization and its application to antenna array design
Integrated Computer-Aided Engineering
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
Hi-index | 0.00 |
This paper proposes a sufficient improved optimization algorithm based on gravitational search algorithm GSA to handle complex multi-objective MO optimization problems. The main idea behind the GSA is based on the Newton's law which has shown great success in recent years. Nevertheless, the existence of some deficiencies such as dependency of the algorithm on its adjusting parameters, stagnation and the probability of trapping in local optima has reduced the searching ability of the GSA. In order to overcome the above deficiencies, this paper suggests a novel mutation technique which is implemented based on four types of solutions. The first type belongs to the non-dominated solutions. The second type includes those solutions which are dominated by only one solution in the search space. Similarly, the third and the forth types belong to those solutions which are dominated by only three and four solutions respectively. After that, the initial GSA population is constructed by choosing from these four types of solutions. This procedure will let the next movement of the algorithm to reach more optimal solutions through the improvisation stage. The satisfying performance of the proposed algorithm is examined on several well-known benchmarks including both the single-objective and multi-objective optimization problems.