A semi-autonomous particle swarm optimizer based on gradient information and diversity control for global optimization

• Published in: Applied soft computing Vol. 69; pp. 330 - 343
• Main Authors: Santos, Reginaldo, Borges, Gilvan, Santos, Adam, Silva, Moisés, Sales, Claudomiro, Costa, João C.W.A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-08-2018
• Subjects: Global optimization; Gradient descent; Multimodal optimization; Newton's method; Particle swarm optimization (PSO); Gradient descent; Newton's method; Global optimization; Multimodal optimization; Particle swarm optimization (PSO)
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.04.027

•A semi-autonomous particle swarm optimizer (SAPSO) is proposed for global optimization.•The SAPSO algorithm uses gradient-based information and diversity control to properly investigate prominent areas of the search space.•A scheme of attraction and repulsion can cluster and repel particles during the search process.•The SAPSO algorithm revealed competitive results when is applied on benchmark optimization problems.
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.