The author proposes an extension of particle swarm optimization (PSO) for solving interval-valued optimization problems and applies the extended PSO to evolutionary training of neural networks (NNs) with interval weights. In the proposed PSO, values in the genotypes are not real numbers but intervals. Experimental results show that interval-valued NNs trained by the proposed method could well a...

This paper presents the application of the PSO (Particle Swarm Optimization) as a tool to solve the Traveling Salesman Problem. The proposed application consists of using a technique based on Artificial Intelligent – AI. It is based in the simulation of social interaction between individuals of a population. Here, each element of population moves in the hyper-space and they are attracted by pos...

This paper presents the swarm intelligence (SI)-based particle swarm optimisation (PSO) in context of designing infinite-impulse response (IIR) digital filters. IIR filters are the part of digital filters with recursive responses. Since the error surface of IIR digital filters is generally non-linear and multimodal, global optimisation techniques are required in order to avoid local minima. The...

Particle Swarm Optimization (PSO) is a technique used to explore the search space of a given problem to find the settings or parameters required to maximize a particular objective. This technique, first described by James Kennedy and Russell C. Eberhart in 1995 [1], originates from two separate concepts: the idea of swarm intelligence based off the observation of swarming habits by certain kind...

In this paper a new effective optimization algorithm called hybrid particle swarm optimizer with breeding and subpopulation is presented. This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, now in use for the optimization of electromagnetic structures, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social r...

This work gives an overview of the broad field of computational swarm intelligence and its applications in swarm robotics. Computational swarm intelligence is modelled on the social behavior of animals and its principle application is as an optimization technique. Swarm robotics is a relatively new and rapidly developing field which draws inspiration from swarm intelligence. It is an interestin...

The job-shop scheduling problem has attracted many researchers’ attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of par...

Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, some of them, such as Particle Swarm Optimization, may not present the ability to generate diversity after environmental changes. In this paper we propose a hybrid algorithm to overcome this problem by applying a very interesting feature of the Fish School Search algorithm to the Particle Swarm ...

This paper empirically evaluates four meta-heuristic search techniques namely particle swarm optimization, artificial bee colony algorithm, Genetic Algorithm and Big Bang Big Crunch Algorithm for automatic test data generation for procedure oriented programs using structural symbolic testing method. Test data is generated for each feasible path of the programs. Experiments on ten benchmark prog...

This paper assumes each user has more than one queue, derives a new packet dependent proportional fairness power allocation pattern based on the sum of weight capacity and the packet’s priority in users’ queues, and proposes 4 new schemes of cross-layer packet dependent OFDM scheduling for heterogeneous classes of traffic based on proportional fairness. Scenario 1, scenario 2 and scenario 3 lea...