A distributed variant of multi-objective particle swarm optimization called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) was used to develop a new optimization-based synthesis routine for Grashof mechanisms. By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing objective functions is removed and the ...

so far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is particle swarm optimization (pso). prior some efforts by applying fuzzy logic for improving defects of pso such as trapping in local optimums and early convergence has been done. moreover to overcome the problem of i...

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...

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...

<span>Worldwide, the COVID-19 widespread has significant impact on a great number of people. The hospital admittance issue for patients with been optimized by previous research. Identifying symptoms that can be used to determine patient's health status, whether they are dead or alive it is difficult task medical professionals. To solve this issue, multi-objective group counselling optimiz...