Name
Title | ||
|---|---|---|
Multi-objective differential evolution based on normalization and improved mutation strategy. |
Abstract | ||
|---|---|---|
Developing efficient algorithms for solving multi-objective optimization problems is a challenging and essential task in many applications. This task involves two or more conflicting objectives that need to be simultaneously optimized. Many real-world problems fall into this category. We introduce an improved version of multi-objective differential evolution (DE) algorithm, namely MODE that uses a new mutation strategy and a normalization method to select non-dominated solutions. The new mutation strategy “DE/rand-to-best” uses the best normalized individual in terms of all the objectives to guide the search towards the true pareto optimal solutions. As a result, the probability of producing superior solutions is increased and a faster convergence is achieved. Summation of normalized objective values method is used instead of non-domination sorting to overcome the high computational complexity and overhead problems of sorting non-dominated solutions. The performance of our approach is tested on a set of benchmark problems that consist of two to five objectives. Different combinations of multi-objective evolutionary programming and multi-objective differential evolution algorithms have been used for comparisons. The results affirm the efficiency and robustness of the proposed approach among other well-known algorithms from the literature. |
Year | DOI | Venue |
|---|---|---|
2017 | https://doi.org/10.1007/s11047-016-9585-y | Natural Computing |
Keywords | Field | DocType |
Multi-objective optimization problems,Differential evolution,Summation of normalized objective values method and multi-objective evolutionary programming,90C27,74P99,13P25,65K10,80M50 | Convergence (routing),Normalization (statistics),Robustness (computer science),Artificial intelligence,Evolutionary programming,Optimization problem,Mathematical optimization,Algorithm,Differential evolution,Sorting,Machine learning,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
16 | 4 | 1567-7818 |
Citations | PageRank | References |
0 | 0.34 | 33 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noor H. Awad | 1 | 159 | 8.15 |
| Mostafa Z. Ali | 2 | 252 | 19.32 |
| Rehab M. Duwairi | 3 | 85 | 10.79 |