Name
Abstract | ||
|---|---|---|
A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1162/EVCO_a_00038 | Evolutionary Computation |
Keywords | Field | DocType |
multi-objective optimization problem,multiple subproblems,evolutionary multi-objective optimization,adaptive evolutionary multi-objective approach,weight vector,search direction,multi-objective metaheuristic algorithm,advanced local search technique,simulated annealing,salesman problem,multi-objective knapsack problem,search performance,local search,knapsack problem,markov chain,metaheuristics,traveling salesman problem,stochastic process,combinatorial optimization | Simulated annealing,Hill climbing,Mathematical optimization,Parallel metaheuristic,Combinatorial optimization,Artificial intelligence,Local search (optimization),2-opt,Mathematics,Machine learning,Tabu search,Metaheuristic | Journal |
Volume | Issue | ISSN |
19 | 4 | 1530-9304 |
Citations | PageRank | References |
75 | 1.64 | 21 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hui Li | 1 | 78 | 2.02 |
| Dario Landa Silva | 2 | 316 | 28.38 |