We investigate the runtime of the Binary Particle Swarm Optimization (PSO) algorithm introduced by Kennedy and Eberhart (1997). The Binary PSO maintains a global best solution and a swarm of particles. Each particle consists of a current position, an own best position and a velocity vector used in a probabilistic process to update the particle’s position. We present lower bounds for a broad cla...

The steady growth of e-commerce has led to a significantly large number of reviews for a product or service. This gives useful information to the users to take an informed decision on whether to acquire a service and/or product or not. Opinion mining techniques are used to automatically process customer reviews for extracting feature and opinion in a concise summary form. Existing feature based...

A novel optimization algorithm, called accelerated particle swarm optimization (APSO), is proposed to be used for dynamic spectrum sensing in cognitive radio networks. While modified swarm-based optimization algorithms focus on slight variations of the standard mathematical formulas, in APSO, the acceleration variable of the particles is also considered in the search space. We show that the pro...

This paper develops an effective method for a probabilistic analysis of earthen slopes. Typically, the safety of a slope is assessed in terms of a factor of safety based on deterministic methods. However, these methods are unable to handle the uncertainties in the stability analysis. Therefore, the probabilistic approach is used for analyzing the safety of slopes, which is capable to take into ...

A new global path planning approach based on particle swarm optimization (PSO) for a mobile robot in a static environment is presented. Consider path planning as an optimization problem with constraints. The constraints are the path can not pass by the obstacles. The optimization target is the path is shortest. The obstacles in the robot’s environment are described as polygons and the vertexes ...

This paper introduces an effectual technique to solve the DNA sequence assembly problem using a variance of the standard Particle Swarm Optimization (PSO) called the Constriction factor Particle Swarm Optimization (CPSO).The problem of sequence assembly is one of the primary problems in computational molecular biology that requires optimization methodologies to rebuild the original DNA sequence...

The problem of development of the SVM classifier based on the modified particle swarm optimization has been considered. This algorithm carries out the simultaneous search of the kernel function type, values of the kernel function parameters and value of the regularization parameter for the SVM classifier. Such SVM classifier provides the high quality of data classification. The idea of particle...

Evolutionary Algorithms (EAs) are a set of probabilistic optimization algorithms based on an analogy between natural biological systems and engineered systems. In this paper, the computational performance a set of specific EAs (specifically, the Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization, Ant Colony Optimization and Shuffled Complex Evolution Algorithm) are compare...

Particle swarm optimization (PSO) has shown to be a robust and efficient optimization algorithm therefore PSO has received increased attention in many research fields. This paper demonstrates the feasibility of applying the Dynamic Inertia Weight Particle Swarm Optimization to solve a Non-Polynomial (NP) Complete puzzle. This paper presents a new approach to solve the Nonograms Puzzle using Dyn...