Hierarchical dynamic neighborhood based Particle Swarm Optimization for global optimization (original) (raw)

Global and Local Neighborhood Based Particle Swarm Optimization

Harmony Search and Nature Inspired Optimization Algorithms, 2018

The particle swarm optimization (PSO) is one of the popular and simple to implement swarm intelligence based algorithms. To some extent, PSO dominates other optimization algorithms but prematurely converging to local optima and stagnation in later generations are some pitfalls. The reason for these problems is the unbalancing of the diversification and convergence abilities of the population during the solution search process. In this paper, a novel position update process is developed and incorporated in PSO by adopting the concept of the neighborhood topologies for each particle. Statistical analysis over 15 complex benchmark functions shows that performance of propounded PSO version is much better than standard PSO (PSO 2011) algorithm while maintaining the cost-effectiveness in terms of function evaluations.

A Clustering Particle Swarm Optimizer for Dynamic Optimization

2009

In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments.

Tutorial on particle swarm optimization and its combinations to other evolutionary algorithms

Open Science Framework (OSF) Preprints, 2022

Local optimization with convex function is solved perfectly by traditional mathematical methods such as Newton-Raphson and gradient descent but it is not easy to solve the global optimization with arbitrary function although there are some purely mathematical approaches such as approximation, cutting plane, branch and bound, and interval method which can be impractical because of their complexity and high computation cost. Recently, some evolutional algorithms which are inspired from biological activities are proposed to solve the global optimization by acceptable heuristic level. Among them is particle swarm optimization (PSO) algorithm which is proved as an effective and feasible solution for global optimization in real applications. Although the ideology of PSO is not complicated, it derives many variants, which can make new researchers confused. Therefore, this tutorial focuses on describing, systemizing, and classifying PSO by succinct and straightforward way. Moreover, combinations of PSO and other evolutional algorithms for improving PSO itself or solving other advanced problems are mentioned too.

Compound particle swarm optimization in dynamic environments

2008

Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO) is proposed as a new variant of particle swarm optimization to enhance its performance in dynamic environments. Within CPSO, compound particles are constructed as a novel type of particles in the search space and their motions are integrated into the swarm. A special reflection scheme is introduced in order to explore the search space more comprehensively. Furthermore, some information preserving and anti-convergence strategies are also developed to improve the performance of CPSO in a new environment. An experimental study shows the efficiency of CPSO in dynamic environments.

Improved particle swarm algorithms for global optimization

Applied Mathematics and Computation, 2008

Particle swarm optimization algorithm has recently gained much attention in the global optimization research community. As a result, a few variants of the algorithm have been suggested. In this paper, we study the efficiency and robustness of a number of particle swarm optimization algorithms and identify the cause for their slow convergence. We then propose some modifications in the position update rule of particle swarm optimization algorithm in order to make the convergence faster. These modifications result in two new versions of the particle swarm optimization algorithm. A numerical study is carried out using a set of 54 test problems some of which are inspired by practical applications. Results show that the new algorithms are much more robust and efficient than some existing particle swarm optimization algorithms. A comparison of the new algorithms with the differential evolution algorithm is also made.

An improved local best searching in Particle Swarm Optimization using Differential Evolution

2011

Particle Swarm Optimization (PSO) has achieved remarkable attentions for its capability to solve diverse global optimization problems. However, this method also shows several limitations. PSO easily trapped in the global optimum and often required vast computational cost when solving high dimensional problems. Therefore, we propose some modifications to overcome these issues. In this work, Differential Evolution (DE) mutation and crossover operations are implemented to improve local best particles searching in PSO. A numerical analysis is carried out using benchmark functions and is compared with standard PSO and DE method. Results presented suggest the prospective of our proposed method.

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.

Differential Evolution Based Particle Swarm Optimization

2007 IEEE Swarm Intelligence Symposium, 2007

The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. The performance of the proposed approach is investigated and compared with differential evolution, a Von Neumann particle swarm optimizer and a barebones particle swarm optimizer. The experiments conducted show that the BBDE provides excellent results with the added advantage of little, almost no parameter tuning. Moreover, the performance of the barebones differential evolution using the ring and Von Neumann neighborhood topologies is investigated. Finally, the application of the BBDE to the real-world problem of unsupervised image classification is investigated. Experimental results show that the proposed approach performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.