Diversity enhanced particle swarm optimization with neighborhood search (original) (raw)
Related papers
2013
The problem of early convergence in the Particle Swarm Optimization (PSO) algorithm often causes the search process to be trapped in a local optimum. This problem often occurs when the diversity of the swarm decreases and the swarm cannot escape from a local optimal. In this paper, a novel dynamic diversity enhancement particle swarm optimization (DDEPSO) algorithm is introduced. In this variant of PSO, we periodically replace some of the swarm's particles by artificial ones, which are generated based on the history of the search process, in order to enhance the diversity of the swarm and promote the exploration ability of the algorithm. Afterwards, we update the velocity of the artificial particles in corresponding generating period by a new velocity equation with the minimum inertia weight in order to enhance the exploitation potentiality of the swarm. The performance of this approach has been tested on the set of twelve standard unimodal and multimodal (Rotated or unrotated) benchmark problems and the results have been compared with our previous work as well as four other variants of the PSO algorithm. The numerical results demonstrate that the proposed algorithm outperforms others in most of the test cases taken in this study.
Diversity control in particle swarm optimization
… Intelligence (SIS), 2011 IEEE Symposium on, 2011
Population diversity of particle swarm optimization (PSO) is important when measuring and dynamically adjusting algorithm's ability of "exploration" or "exploitation". Population diversities of PSO based on L1 norm are given in this paper. Useful information on search process of an optimization algorithm could be obtained by using this measurement. Properties of PSO diversity based on L1 norm are discussed. Several methods for diversity control are tested on benchmark functions, and the method based on current position and average of current velocities has the best performance. This method could control the PSO diversity effectively and gets better performance than the standard PSO.
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 new diversity guided particle swarm optimization with mutation
2009
Abstract This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing premature convergence.
Information Sciences, 2015
The paper presents a novel paradigm of the original particle swarm concept, based on the idea of having two types of agents in the swarm; the ‘‘explorers’’ and the ‘‘settlers’’, that could dynamically exchange their role in the search process. The explorers’ task is to continuously explore the search domain, while the settlers set out to refine the search in a promising region currently found by the swarm. To obtain this particle task differentiation, the numerical coefficients of the cognitive and social component of the stochastic acceleration as well as the inertia weight were related to the distance of each particle from the best position found so far by the swarm, each of them with a proper distribution over the swarm. This particle task differentiation enhances the local search ability of the particles closer to gbest and improves the exploration ability of the particles as the distance from gbest increases. The originality of this approach is based on the particle task differentiation and on the dynamical adjustment of the particle velocities at each time step on the basis of the current distance of each particle from the best position discovered so far by the swarm. To ascertain the effectiveness of the proposed variant of the PSO algorithm, several benchmark test functions, both unimodal and multi-modal, have been considered and, thanks to its task differentiation concept and adaptive behavior feature, the algorithm has demonstrated a surprising effectiveness and accuracy in identifying the optimal solution.
A Novel Diversity Guided Particle Swarm Multi-objective Optimization Algorithm
International Journal of Digital Content Technology and its Applications, 2011
This paper presents a mu lti-objective d iversity gu ided Particle Swa rm Op timization a pproach named MOPSO-AR wh ich increases di versity perfo rmance of multi-objective Parti cle S warm optimization by u sing A ttraction a nd Re pulsion (AR) m echanism. AR m echanism us es a d iversity measure to control the swar m. Being attractive and repulsive wil l help to overcome t he problem of premature c onvergence. AR mechanism together wi th cr owding di stance co mputation an d mutation operator maintains the diversity of non-dominated set in external archive. The approach is verified by several te st f unction exper iments. Results demo nstrate t hat the p roposed approach i s highly competitive in distribution of non-dominated solutions but still keeps convergence towards the Pareto front.
Particle Swarm Optimization algorithm based on Diversified Artificial Particles (PSO-DAP)
2013
Speed of convergence in the PSO is very high, and this issue causes to the algorithm can't investigate search space truly, When diversity of the population decreasing, all the population start to liken together and the algorithm converges to local optimal swiftly. In this paper we implement a new idea for better control of the diversity and have a good control of the algorithm's behavior between exploration and exploitations phenomena to preventing premature convergence. In our approach we have control on diversity with generating diversified artificial particles (DAP) and injection them to the population by a particular mechanism when diversity lessening, named Particle Swarm Optimization algorithm based on Diversified Artificial Particles (PSO-DAP). The performance of this approach has been tested on the set of ten standard benchmark problems and the results are compared with the original PSO algorithm in two models, Local ring and Global star topology. The numerical results show that the proposed algorithm outperforms the basic PSO algorithms in all the test cases taken in this study
2014
A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.
Particle swarm optimization: performance tuning and empirical analysis
2009
This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy functions and real life applications in engineering fields. The effectiveness of the algorithms is discussed.