Name
Title | ||
|---|---|---|
A hybrid quantum evolutionary algorithm for solving engineering optimization problems |
Abstract | ||
|---|---|---|
Evolutionary Algorithms (EA) have been successfully employed for solving difficult constrained engineering optimization problems. However, EA implementations often suffer from premature convergence due to the lack of proper balance between exploration and exploitation in the search process. This paper proposes a Hybrid Quantum inspired EA, which balances the exploration and exploitation in the search process by adaptively evolving the populations. It employs an adaptive quantum rotation based crossover operator designed by hybridizing a conventional crossover operator with the principles of Quantum Mechanics. The degree of rotation in this operator is determined adaptively. The proposed algorithm does not require either a mutation operator, to avoid premature convergence, or a local heuristic to improve convergence rate. Further, a parameter-tuning free hybrid technique is employed for handling constraints, which overcomes some limitations in the traditional techniques like penalty factor methods, by hybridizing Feasibility Rules method with Adaptive Penalty Factor method. It is implemented by using two populations, each evolving by applying one of the constraints handling techniques and swapping a part of the populations. A standard set of six diverse benchmark engineering design optimization problems have been used for testing the proposed algorithm. The algorithm exhibits superior performance than the existing state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/HIS-2010-0115 | Int. J. Hybrid Intell. Syst. |
Keywords | Field | DocType |
engineering optimization problem,crossover operator,quantum mechanics,premature convergence,adaptive penalty factor method,hybrid quantum,search process,hybrid quantum evolutionary algorithm,conventional crossover operator,convergence rate,proposed algorithm,mutation operator,optimization problem,adaptive,constrained optimization,evolutionary algorithm | Evolutionary algorithm,Computer science,Rate of convergence,Artificial intelligence,Engineering optimization,Optimization problem,Mathematical optimization,Heuristic,Crossover,Premature convergence,Algorithm,Machine learning,Constrained optimization | Journal |
Volume | Issue | Citations |
7 | 3 | 10 |
PageRank | References | Authors |
0.59 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ashish Mani | 1 | 23 | 6.88 |
| C. Patvardhan | 2 | 78 | 12.28 |