A Particle Swarm Optimizer for Constrained Numerical Optimization
نویسندگان
چکیده
This paper presents a particle swarm optimizer to solve constrained optimization problems. The proposed approach adopts a simple method to handle constraints of any type (linear, nonlinear, equality and inequality), and it also presents a novel mechanism to update the velocity and position of each particle. The approach is validated using standard test functions reported in the specialized literature and it’s compared with respect to algorithms representative of the state-of-the-art in the area. Our results indicate that the proposed scheme is a promising alternative to solve constrained optimization problems using particle swarm optimization.
منابع مشابه
A hybrid constrained optimization approach coupling PSO and adaptive constraint-handling technique
In this paper, we present a novel hybrid approach combining particle swarm optimization (PSO) and adaptive constraint-handling technique (ACT) for solving constrained numerical and engineering optimization problems. The proposed hybrid approach simultaneously adopts particle swarm optimizer and hybrid mutation operators to generate the offspring population. Additionally, the adaptive constraint...
متن کاملConstrained Optimization by Combining the α Constrained Method with Particle Swarm Optimization
Recently, Particle Swarm Optimization (PSO) has been applied to various application fields. In this paper, a new optimization method “α Constrained Particle Swarm Optimizer (αPSO)”, which is the combination of the α constrained method and PSO, is proposed. The αPSO is applied to several test problems such as nonlinear programming problems and problems with non-convex constraints. It is compared...
متن کاملAIAA 2002–1235 Particle Swarm Optimization
The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of...
متن کاملComprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems
This paper presents an improved particle swarm optimizer (PSO) for solving multimodal optimization problems with problem-specific constraints and mixed variables. The standard PSO is extended by employing a comprehensive learning strategy, different particle updating approaches, and a feasibility-based rule method. The experiment results show the algorithm located the global optima in all teste...
متن کاملA hierarchical particle swarm optimizer with latin sampling based memetic algorithm for numerical optimization
Memetic algorithms, one type of algorithms inspired by nature, have been successfully applied to solve numerous optimization problems in diverse fields. In this paper, we propose a new memetic computing model, using a hierarchical particle swarm optimizer (HPSO) and latin hypercube sampling (LHS) method. In the bottom layer of hierarchical PSO, several swarms evolve in parallel to avoid being t...
متن کامل