Name
Title | ||
|---|---|---|
A hybrid multi-mechanism optimization approach for the payload packing design of a satellite module. |
Abstract | ||
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Display Omitted Inspired by \"no free lunch theorem\", the related packing knowledge is obtained to form the positioning rule and ordering rule.A heuristic ant colony optimization approach with adjustment strategy is proposed for the bearing surface packing problem.A particle swarm optimization approach is designed to optimize the mass center and inertia angles of the satellite module in a way of rotation.The solution quality of the proposed hybrid multi-mechanism optimization approach (HMMOA) is better than existing ones for this problem.The solution speed and success ratio of the proposed HMMOA are higher than existing ones for this problem. The payload packing problem of a satellite module (SM3P) belongs to a complex engineering layout and combinatorial optimization problem. SM3P can not be solved effectively by traditional exact methods. Evolutionary algorithms have shown some promise of tackling SM3P in previous work; however, the solution quality and computational efficiency are still challenges. Inspired by previous works (such as divide-and-conquer and no free lunch theorem), this study designs three-stage solution strategy in the light of the characteristics of SM3P and proposes a hybrid multi-mechanism optimization approach (HMMOA) integrating knowledge heuristic rules with two evolutionary algorithms such as ant colony optimization (ACO) and particle swarm optimization (PSO) in different stages. Firstly, the payloads to be placed are assigned to different bearing surfaces in the distribution stage. Then SM3P is decomposed into several subproblems solved by the heuristic ACO algorithm in the second stage, where a better feasible packing scheme obtained by the knowledge-based heuristic ACO is further improved by a heuristic adjustment strategy. At last, the solutions of different subproblems are combined to form a whole solution that is optimized by PSO in a way of rotation to minimize both errors of the mass center and inertia angle while other design objectives remain unchanged. The experimental results illustrate the capability of the proposed HMMOA in tackling the complex problem with better solution quality while less computational effort. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.asoc.2016.04.006 | Appl. Soft Comput. |
Keywords | Field | DocType |
Heuristic method,Layout optimization problem,Ant colony optimization,Quasi-physical algorithm,Adjustment strategy | Ant colony optimization algorithms,Particle swarm optimization,Mathematical optimization,Heuristic,Evolutionary algorithm,Extremal optimization,Multi-swarm optimization,Optimization problem,Mathematics,Metaheuristic | Journal |
Volume | Issue | ISSN |
45 | C | 1568-4946 |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ziqiang Li | 1 | 4 | 0.77 |
| Yuan Zeng | 2 | 56 | 8.39 |
| Yi-shou Wang | 3 | 7 | 2.54 |
| Lu Wang | 4 | 1 | 0.35 |
| Bowen Song | 5 | 96 | 11.08 |