Penalty guided genetic search for reliability design optimization
Computers and Industrial Engineering
Genetic algorithm for non-linear mixed integer programming problems and its applications
Computers and Industrial Engineering
Reliability optimal design problem with interval coefficients using hybrid genetic algorithms
Proceedings of the 23rd international conference on on Computers and industrial engineering
GA-based reliability design: state-of-the-art survey
Computers and Industrial Engineering
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Improved Imperialist Competitive Algorithm for Constrained Optimization
IFCSTA '09 Proceedings of the 2009 International Forum on Computer Science-Technology and Applications - Volume 01
A novel global harmony search algorithm for reliability problems
Computers and Industrial Engineering
Imperialist competitive algorithm for minimum bit error rate beamforming
International Journal of Bio-Inspired Computation
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
A coevolutionary differential evolution with harmony search for reliability-redundancy optimization
Expert Systems with Applications: An International Journal
A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
System reliability analysis and optimization are important to efficiently utilize available resources and to develop an optimal system design architecture. System reliability optimization has been solved by using optimization techniques including meta-heuristics. Meanwhile, the development of meta-heuristics has been an active research field of the reliability optimization wherein the redundancy, the component reliability, or both are to be determined. In recent years, a broad class of stochastic meta-heuristics, such as simulated annealing, genetic algorithm, tabu search, ant colony, and particle swarm optimization paradigms, has been developed for reliability-redundancy optimization of systems. Recently, a new kind of evolutionary algorithm called Imperialist Competitive Algorithm (ICA) was proposed. The ICA is based on imperialistic competition where the populations are represented by countries, which are classified as imperialists or colonies. However, the trade-off between the exploration (i.e. the global search) and the exploitation (i.e. the local search) of the search space is critical to the success of the classical ICA approach. An improvement in the ICA by implementing an attraction and repulsion concept during the search for better solutions, the AR-ICA approach, is proposed in this paper. Simulations results demonstrates the AR-ICA is an efficient optimization technique, since it obtained promising solutions for the reliability redundancy allocation problem when compared with the previously best-known results of four different benchmarks for the reliability-redundancy allocation problem presented in the literature.