This paper presents a designing an optimal adaptive controller for tracking down the control of robot manipulators based on particle swarm optimization (PSO) algorithm. PSO algorithm has been used to optimize parameters of the controller and hence to minimize the integral square of errors (ISE) as a performance criteria. In this paper, an improved PSO using a logic is proposed to increase the c...

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to pro...

Biogeography is the study of distribution of biological species, over space and time, among random habitats. Recently developed Biogeography-Based Optimization (BBO) is a technique, where solutions of the problem under consideration are named as habitats; similar to chromosome in Genetic Algorithms (GAs) and particles in Particle Swarm Optimization (PSO). Feature sharing among various habitats,...

Abstract Throughout the centuries, nature has been a source of inspiration, with much still to learn from and discover about. Among many others, Swarm Intelligence (SI), substantial branch Artificial Intelligence, is built on intelligent collective behavior social swarms in nature. One most popular SI paradigms, Particle Optimization algorithm (PSO), presented this work. Many changes have made ...

This paper investigates into hybridization between PSO and self-adaptive evolutionary programming techniques for solving economic dispatch (ED) problem with non-smooth cost curves where conventional gradient based methods are in-applicable. The convergence capability of evolutionary programming technique is enhanced with hybridization of self-adaptive evolutionary programming technique with PSO...

this paper presents designing an optimal adaptive controller for tracking control of robot manipulators based on particle swarm optimization (pso) algorithm. pso algorithm has been employed to optimize parameters of the controller and hence to minimize the integral square of errors (ise) as a performance criteria. in this paper, an improved pso using logic is proposed to increase the convergenc...

Curriculum sequencing problem is crucial to e-learning system, which a NP-hard optimization and commonly solved by swarm intelligence. As form of intelligence, particle (PSO) widely used in various kinds problems. However, PSO found ineffective complex The main reason that diversity preservation, leading high risks be trapped the local optima. To solve this problem, novel hybrid algorithm propo...

Complex optimization (CO) problems have been solved using swarm intelligence (SI) methods. One of the CO is Wireless Sensor Network (WSN) coverage problem, which plays an important role in Internet Things (IoT). A novel hybrid algorithm proposed, named particle butterfly (HPSBA), by combining their strengths (PSO) and (BOA), for solving this problem. Significantly, value individual scent intens...

This paper presents a new mutation operator called the Sobol Mutation (SOM) operator for enhancing the performance of Quantum Particle Swarm Optimization (QPSO) algorithm. The SOM operator unlike most of its contemporary mutation operators do not use the random probability distribution for perturbing the swarm population, but uses a quasi random Sobol sequence to find new solution vectors in th...

In this paper we propose an improved self-adaptive artificial bee colony algorithm (IS-ABC) for accurate numerical function optimisation. A modified self-adaptive mechanism based on 1/5th success rule is embedded in the structure of ABC to enhance the speed of the algorithm. The proposed algorithm has been tested on various numerical benchmark functions including the non-traditional functions p...