Particle Swarm Optimisation with Gradually Increasing Directed Neighbourhoods (original) (raw)

Randomized directed neighborhoods with edge migration in particle swarm optimization

Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

A key feature of Particle Swarm Optimization algorithms is that fitness information is shared with individuals in a particle's neighborhood. The kind of neighborhood structure that is used affects the rate at which information is disseminated throughout the population. Existing work has studied global and simple local topologies, as well as more complex, but fixed neighborhood structures. This paper looks at randomly generated, directed graph structures in which information flows in one direction only, and also outgoing edges randomly migrate from one source node to another. Experimental evidence indicates that this random dynamic topology, when used with an inertia weight PSO, performs competitively with some existing methods and outperforms others.

Particle swarm optimization with increasing topology connectivity

Engineering Applications of Artificial Intelligence, 2014

In this paper, we propose a new variant of particle swarm optimization (PSO), namely PSO with increasing topology connectivity (PSO-ITC), to solve unconstrained single-objective optimization problems with continuous search space. Specifically, an ITC module is developed to achieve better control of exploration/exploitation searches by linearly increasing the particle's topology connectivity with time as well as performing the shuffling mechanism. Furthermore, we introduce a new learning framework that consists of a new velocity update mechanism and a new neighborhood search operator that aims to enhance the algorithm's searching performance. The proposed PSO-ITC is extensively evaluated across 20 benchmark functions with various features as well as two engineering design problems. Simulation results reveal that the performance of the PSO-ITC is superior to nine other PSO variants and six cuttingedge metaheuristic search algorithms.

Towards a Network-based Approach to Analyze Particle Swarm Optimizers

In Particle Swarm Optimizers (PSO), the way particles communicate plays an important role on their search behavior influencing the trade-off between exploration and exploitation. The interactions boundaries defined by the swarm topology is an example of this influence. For instance, a swarm with the ring topology tends to explore the environment more than with the fully connected global topology. On the other hand, more connected topologies tend to present a higher exploitation capability. We propose that the analysis of the particles interactions can be used to assess the swarm search mode, without the need for any particles properties (e.g. the particle's position, the particle's velocity, etc.). We define the weighted swarm influence graph I tw t that keeps track of the interactions from the last tw iterations before a given iteration t. We show that the search mode of the swarm does have a signature on this graph based on the analysis of its components and the distribution of the node strengths.

A Statistical Study of the Effects of Neighborhood Topologies in Particle Swarm Optimization

Studies in Computational Intelligence, 2011

The behavior of modern meta-heuristics is directed by both, the variation operators, and the values selected for the parameters of the approach. Particle swarm optimization (PSO) is a meta-heuristic which has been found to be very successful in a wide variety of optimization tasks. In PSO, a swarm of particles fly through hyper-dimensional search space being attracted by both, their personal best position and the best position found so far within a neighborhood. In this paper, we perform a statistical study in order to analyze whether the neighborhood topology promotes a convergence acceleration in four PSO-based algorithms: the basic PSO, the Bare-bones PSO, an extension of BBPSO and the Bare-bones Differential Evolution. Our results indicate that the convergence rate of a PSO-based approach has a strongly dependence of the topology used. We also found that the topology most widely used is not necessarily the best topology for every PSO-based algorithm.

A Modified Particle Swarm Optimization with Random Activation for Increasing Exploration

Kursor

Particle Swarm Optimization (PSO) is a popular optimization technique which is inspired by the social behavior of birds flocking or fishes schooling for finding food. It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in 1995. However, the standard PSO has a shortcoming, i.e., premature convergence and easy to get stack or fall into local optimum. Inertia weight is an important parameter in PSO, which significantly affect the performance of PSO. There are many variations of inertia weight strategies have been proposed in order to overcome the shortcoming. In this paper, a new modified PSO with random activation to increase exploration ability, help trapped particles for jumping-out from local optimum and avoid premature convergence is proposed. In the proposed method, an inertia weight is decreased linearly until half of iteration, and then a random number for an inertia weight is applied until the end of iteration. To emphasis the role of this new inertia ...

Particle swarm optimization with spatially meaningful neighbours

2008 IEEE Swarm Intelligence Symposium, SIS 2008, 2008

Neighbourhood topologies in particle swarm optimization (PSO) are typically random in terms of the spatial positions of connected neighbours. This study explores the use of spatially meaningful neighbours for PSO. An approach is designed which uses heuristics to leverage the natural neighbours computed with Delaunay triangulation. The approach is compared to standard PSO sociometries and fitness distance ratio approaches. Although intrinsic properties of Delaunay triangulation limit the practical application of this approach to low dimensions results show that it is a successful particle swarm optimizer.

Varying the Topology and Probability of Re-Initialization in Particle Swarm Optimization

2005

This paper introduces two new versions of dissipative particle swarm optimization. Both of these use a new time-dependent strategy for randomly re-initializing the positions of the particles. In addition, one variation also uses a novel dynamic neighbourhood topology based on small world networks. We present results from applying these algorithms to two well-known function optimization problems. Both algorithms perform considerably better than both standard PSO and the original dissipative PSO algorithms.

Randomization in particle swarm optimization for global search ability

Expert Systems with Applications, 2011

This paper introduces a novel particle swarm optimization (PSO) with random position to improve the global search ability of particle swarm optimization with linearly decreasing inertia weight (IWPSO). Standard particle swarm optimization and most of its derivations are easy to fall into local optimum of the problem by lacking of mutation in those operations. Inspired by the acceptance probability in simulated annealing algorithm, the random factors could be put in particle swarm optimization appropriately. Consequently, the concept of the mutation is introduced to the algorithm, and the global search ability would be improved. A particle swarm optimization with random position (RPPSO) is tested using seven benchmark functions with different dimensions and compared with four well-known derivations of particle swarm optimization. Experimental results show that the proposed particle swarm optimization could keep the diversity of particles, and have better global search performance.

Particle swarm optimization with adaptive time-varying topology connectivity

Applied Soft Computing, 2014

Particle swarm optimization (PSO) has shown its competitive performance for solving benchmark and real-world optimization problems. Nevertheless, it requires better control of exploration/exploitation searches to prevent the premature convergence of swarms. Thus, this paper proposes a new PSO variant called PSO with adaptive time-varying topology connectivity (PSO-ATVTC) that employs an ATVTC module and a new learning framework. The proposed ATVTC module specifically aims to balance the algorithm's exploration/exploitation searches by varying the particle's topology connectivity with time according to its searching performance. The proposed learning framework consists of a new velocity update mechanism and a new neighborhood search operator to improve the algorithm's performance. A comprehensive study was conducted on 24 benchmark functions and one real-world problem. Compared with nine well-established PSO variants and six other cutting-edge metaheuristic search algorithms, the searching performance of PSO-ATVTC was proven to be more prominent in majority of the tested problems.

Revisiting Population Structure and Particle Swarm Performance

Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, 2018

Population structure strongly affects the dynamic behavior and performance of the particle swarm optimization (PSO) algorithm. Most of PSOs use one of two simple sociometric principles for defining the structure. One connects all the members of the swarm to one another. This strategy is often called gbest and results in a connectivity degree k = n, where n is the population size. The other connects the population in a ring with k = 3. Between these upper and lower bounds there are a vast number of strategies that can be explored for enhancing the performance and adaptability of the algorithm. This paper investigates the convergence speed, accuracy, robustness and scalability of PSOs structured by regular and random graphs with 3≤k≤n. The main conclusion is that regular and random graphs with the same averaged connectivity k may result in significantly different performance, namely when k is low.