Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.

Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem

Abstract: Industries utilize two-sided assembly lines for producing large-sized volume products such as cars and trucks. By employing robots, industries achieve a high level of automation in the assembly pro...

Economic Allocation for Energy Storage System Considering Wind Power Distribution

Abstract: Energy storage systems play a significant role in both distributed power systems and utility power systems Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production Furthermore, the probability cost analysis is first put forward in this paper The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution The IEEE 30-bus system is adopted to perform case studies The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method

A Parallel Particle Swarm Optimization Algorithm with Communication Strategies

Abstract: Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.

Particle swarm with extended memory for multiobjective optimization

Abstract: This paper presents a modified dynamic neighborhood particle swarm optimization (DNPSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. An extended memory is introduced to store global Pareto optimal solutions to reduce computation time. Several benchmark cases were tested and the results show that the modified DNPSO is much more efficient than the original DNPSO and other multiobjective optimization techniques.

Truss optimization with dynamic constraints using a particle swarm algorithm

Abstract: In this paper, a structural truss mass optimization on size and shape is performed taking into account frequency constraints. It is well-known that structural optimizations on shape and size are highly non-linear dynamic optimization problems since this mass reduction conflicts with the frequency constraints especially when they are lower bounded. Besides, vibration modes may switch easily due to shape modifications. This paper intends to investigate the use of a particle swarm optimization (PSO) algorithm as an optimization engine in this type of problem. This choice is based on reported well-behavior of such algorithm as global optimizer in other areas of knowledge. Another feature of the algorithm is taken into account for this choice, like the fact that it is not gradient based, but just based on simple objective function evaluation. The algorithm is briefly revised highlighting its most important features. It is presented four examples regarding the optimization of trusses on shape and size with frequency constraints. The examples are widely reported and used in the related literature as benchmarks. The results show that the algorithm performed similar to other methods and even better in some cases.