Particle swarm optimization techniques for solving numerical and engineeing optimization problems

Abstract

The success of technology is mostly dependent on how well the real world appli- cations or problems are formulated, controlled and optimized. The complexities associated with real world problems are increasing day by day. The real world problems are often characterized by noisy, incomplete data or multimodality due to their inflexible construction. This demands a robust and efficient optimization or computational paradigm. Since the conventional optimization algorithms do not provide good solutions while optimizing nondifferentiable, nonseparable, dis- continuous, discrete and multimodal problems, nature inspired algorithms are the most sorted out paradigms for handling such real world problems. The nature inspired algorithms have evolved over decades and often contain many simple in- dividuals which when work together, produce complex emergent behavior and can be used to solve complex optimization problems. Among many nature inspired algorithms, the Swarm Intelligence (SI) is widely used over decades. SI is an in- novative, distributed and intelligent paradigm for solving optimization problems, developed from the inspiration of biological phenomena such as swarming, flocking and herding of different entities. One of the very simple SI based computational algorithm is Particle Swarm Optimization (PSO). It is a decade old concept in the optimization domain introduced in 1995 by Kennedy and Eberhart. Being a stochastic algorithm it exhibits many similarities with the other evolutionary algorithms. PSO essentially imitates the food foraging behavior of swarm of birds or school of fish. The main source of swarm’s search capability is the interaction among the individuals and the reaction to others experience in reaching the goal. Almost all the real world optimization problems can be modeled as any of the four types of optimization problems; i) Single Objective Unconstrained, ii) Single Objective Constrained, iii) Multi Objective Unconstrained and iv) Multi Objective Constrained problem. Though many variants of PSO are being developed to solve these kind of problems, it still suffers with major problems like premature