Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction
| Autor: | Prahlad Vadakkepat, Arrchana Muruganantham, Kay Chen Tan |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Mathematical optimization Optimization problem Computer science Evolutionary algorithm 02 engineering and technology Kalman filter Multi-objective optimization Evolutionary computation Computer Science Applications Human-Computer Interaction 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Electrical and Electronic Engineering Metaheuristic Software Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 46:2862-2873 |
| ISSN: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tcyb.2015.2490738 |
| Popis: | Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance. |
| Databáze: | OpenAIRE |
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