As the computer technology improves rapidly, the scale of software has increased greatly, which makes it more and more difficult to find a bug in software. As a result, the enhancement of software quality and reliability has become an important task in the field of software engineering. Test is an important step that guarantees software quality and reliability. We put forward a novel multi-obje...

In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOP...

This paper presents a demonstration of solving combined economic and emission dispatch problems by using one of swarm intelligences, called particle swarm optimization. The objective of the combined problem can be expressed by taking both the total production cost and total emission into account with required constraints. Among potential intelligent search methods, particle swarm optimization i...

In this paper, particle swarm optimization, which is a recently developed evolutionary algorithm, is used to optimize machining parameters in hard turning processes where multiple conflicting objectives are present. The relationships between machining parameters and the performance measures of interest are obtained by using experimental data and swarm intelligent neural network systems (SINNS)....

This paper introduces a hybrid metaheuristic, the Variable Neighborhood Particle Swarm Optimization (VNPSO), consisting of a combination of the Variable Neighborhood Search (VNS) and Particle Swarm Optimization(PSO). The proposed VNPSO method is used for solving the multi-objective Flexible Job-shop Scheduling Problems (FJSP). The details of implementation for the multi-objective FJSP and the c...

In this paper, a methodology is presented for optimum shape design of double-layer grids subject to gravity and earthquake loadings. The design variables are the number of divisions in two directions, the height between two layers and the cross-sectional areas of the structural elements. The objective function is the weight of the structure and the design constraints are some limitations on str...

This paper presents a dynamic particle swarm optimization based search for optimal fusion configuration of sensors in distributed detection network in presence of a nonstationary binary symmetric channel. The wireless channel in sensor networks is a non-stationary random process, which moves the optima of the original problem, otherwise static. The optimal fusion configuration minimizes the pro...

Particle swarm optimization is an efficient population based algorithm used to solve real valued and nonlinear continuous optimization problems. To resolve binary optimization problems with PSO, binary particle swarm optimization (BPSO) has been developed. In this paper, optimum design of plates using BPSO is presented. The objective function aims at finding the optimum weight of plates with th...

Swarm intelligence (SI) is widely used in many complex optimization problems. It is a collective behavior of social systems such as honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). This paper presents a detailed overview of Particle Swarm Optimization (PSO), its variants and hybridization of PSO with Bee Algorithm (BA). This paper also surveys various SI techniques p...