A semi-autonomous particle swarm optimizer based on gradient information and diversity control for global optimization
Autor: | Gilvan Borges, Claudomiro Sales, Reginaldo Santos, João C. W. A. Costa, Moisés Silva, Adam Santos |
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Rok vydání: | 2018 |
Předmět: |
0209 industrial biotechnology
Mathematical optimization Optimization problem Computer science Control (management) MathematicsofComputing_NUMERICALANALYSIS Particle swarm optimization 02 engineering and technology Random walk 020901 industrial engineering & automation Local optimum Genetic algorithm 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Gradient descent Global optimization Software |
Zdroj: | Applied Soft Computing. 69:330-343 |
ISSN: | 1568-4946 |
Popis: | The deterministic optimization algorithms far outweigh the non-deterministic ones on unimodal functions. However, classical algorithms, such as gradient descent and Newton's method, are strongly dependent on the quality of the initial guess and easily get trapped into local optima of multimodal functions. On the contrary, non-deterministic optimization methods, such as particle swarm optimization and genetic algorithms perform global optimization, however they waste computational time wandering the search space as a result of the random walks influence. This paper presents a semi-autonomous particle swarm optimizer, termed SAPSO, which uses a gradient-based information and diversity control to optimize multimodal functions. The proposed algorithm avoids the drawbacks of deterministic and non-deterministic approaches, by reducing computational efforts of local investigation (fast exploitation with gradient information) and escaping from local optima (exploration with diversity control). The experiments revealed promising results when SAPSO is applied on a suite of test functions based on De Jong's benchmark optimization problems and compared to other PSO-based algorithms. |
Databáze: | OpenAIRE |
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