Probabilistic opposition-based particle swarm optimization with velocity clamping

• Published in: Knowledge and information systems Vol. 39; no. 3; pp. 703 - 737
• Main Authors: Shahzad, Farrukh, Masood, Sohail, Khan, Naveed Kazim
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2014; Springer; Springer Nature B.V
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial intelligence; Benchmarking; Clamping; Computer Science; Computer science; control theory; systems; Convergence; Data Mining and Knowledge Discovery; Database Management; Engineering research; Evolution; Exact sciences and technology; Inertia; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Investigations; IT in Business; Optimization; Optimization algorithms; Performance evaluation; Population; Probabilistic methods; Probability theory; Regular Paper; Studies; Swarm intelligence; Theoretical computing; Velocity; Swarm intelligence; Probabilistic opposition-based learning; Particle swarm optimization; Function Optimization; Complex function; Probabilistic approach; Evolutionary algorithm; Position control; Reinforcement learning; Global optimum; Clamping; Modeling; Work holder; Convergence speed; Convergence rate; Differential evolution; Population size; Inertia; Speed control; Artificial intelligence; Numerical convergence; Mathematical programming
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-013-0624-z

A probabilistic opposition-based Particle Swarm Optimization algorithm with Velocity Clamping and inertia weights (OvcPSO) is designed for function optimization—to accelerate the convergence speed and to optimize solution’s accuracy on standard benchmark functions. In this work, probabilistic opposition-based learning for particles is incorporated with PSO to enhance the convergence rate—it uses velocity clamping and inertia weights to control the position, speed and direction of particles to avoid premature convergence. A comprehensive set of 58 complex benchmark functions including a wide range of dimensions have been used for experimental verification. It is evident from the results that OvcPSO can deal with complex optimization problems effectively and efficiently. A series of experiments have been performed to investigate the influence of population size and dimensions upon the performance of different PSO variants. It also outperforms FDR-PSO, CLPSO, FIPS, CPSO-H and GOPSO on various benchmark functions. Last but not the least, OvcPSO has also been compared with opposition-based differential evolution (ODE); it outperforms ODE on lower swarm population and higher-dimensional functions.