Convergence proof of an enhanced Particle Swarm Optimisation method integrated with Evolutionary Game Theory (original) (raw)

An Enhanced Particle Swarm Optimization Method Integrated With Evolutionary Game Theory

IEEE Transactions on Games, 2018

This paper develops a novel particle swarm optimiser algorithm. The focus of this study is how to improve the performance of the classical particle swarm optimisation approach, i.e., how to enhance its convergence speed and capacity to solve complex problems while reducing the computational load. The proposed approach is based on an improvement of Particle Swarm Optimisation using Evolutionary Game Theory. This method maintains the capability of the particle swarm optimiser to diversify the particles' exploration in the solution space. Moreover, the proposed approach provides an important ability to the optimisation algorithm, that is adaptation of the search direction which improves the quality of the particles based on their experience. The proposed algorithm is tested on a representative set of continuous benchmark optimisation problems and compared with some other classical optimisation approaches. Based on the test results of each benchmark problem, its performance is analysed and discussed.

Novel Multi-swarm Approach for Balancing Exploration and Exploitation in Particle Swarm Optimization

Advances in Intelligent Systems and Computing

Several metaheuristic algorithms and improvements to the existing ones have been presented over the years. Most of these algorithms were inspired either by nature or the behavior of certain swarms, such as birds, ants, bees, or even bats. These algorithms have two major components, which are exploration and exploitation. The interaction of these components can have a significant influence on the efficiency of the metaheuristics. Meanwhile, there are basically no guiding principles on how to strike a balance between these two components. This study, therefore, proposes a new multi-swarm-based balancing mechanism for keeping a balancing between the exploration and exploitation attributes of metaheuristics. The new approach is inspired by the phenomenon of the leadership scenario among a group of people (a group of people being governed by a selected leader(s)). These leaders communicate in a meeting room, and the overall best leader makes the final decision. The simulation aspect of the study considered several benchmark functions and compared the performance of the suggested algorithm to that of the standard PSO (SPSO) in terms of efficiency.

Dynamic Diversity Enhancement in Particle Swarm Optimization (DDEPSO) Algorithm For Preventing from Premature Convergence

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.

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

A Review on Convergence Analysis of Particle Swarm Optimization

International Journal of Swarm Intelligence Research

Particle swarm optimization (PSO) is one of the popular nature-inspired metaheuristic algorithms. It has been used in different applications. The convergence analysis is among the key theoretical studies in PSO. This paper discusses major contributions in the convergence analysis of PSO. A systematic classification will be used for the review purpose. Possible future works are also highlighted as to investigate the performance of PSO variants to deal with COPs through theoretical perspective and general discussions on experimental results on merits of the proposed approach.

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.

A new hybrid multi-agent-based particle swarm optimisation

International Journal of Bio-Inspired …, 2009

This paper presents a multi-agent-based hybrid particle swarm optimisation technique. The algorithm integrates the deterministic, the multi-agent system (MAS) and the particle swarm optimisation (PSO) algorithm. An agent in hybrid multi-agent PSO (HMAPSO) represents a particle to PSO and a candidate solution to optimisation problem. All agents search parallel in an equally distributed lattice-like structure to save energy and computational time. The best solution is obtained through bee decision making process. Thus making use of deterministic search, multi-agent and bee PSO, the HMAPSO realises the purpose of optimisation. The proposed algorithm has been tested on various optimisation problems. The experimental results obtained show the robustness and accuracy of proposed HMAPSO. It also has been concluded that the proposed HMAPSO is able to generate a unique and optimal solution than the earlier reported approaches and hence can be a better option for real-time online optimisation problems. A new hybrid multi-agent-based particle swarm optimisation technique', Int. J.

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

Symmetry, 2022

Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; rou...

A new hybrid multi-agent-based particle swarm optimisation technique

International Journal of Bio-inspired Computation, 2009

The gregarious particle swarm optimizer (G-PSO)

Proceedings of the 8th annual conference on Genetic and evolutionary computation - GECCO '06, 2006

This paper presents a gregarious particle swarm optimization algorithm (G-PSO) in which the particles explore the search space by aggressively scouting the local minima with the help of only social knowledge. To avoid premature convergence of the swarm, the particles are re-initialized with a random velocity when stuck at a local minimum. Furthermore, G-PSO adopts a "reactive" determination of the step size, based on feedback from the last iterations. This is in contrast to the basic particle swarm algorithm, in which the particles explore the search space by using both the individual "cognitive" component and the "social" knowledge and no feedback is used for the self-tuning of algorithm parameters. The novel scheme presented, besides generally improving the average optimal values found, reduces the computation effort.

Convergence proof of an enhanced Particle Swarm Optimisation method integrated with Evolutionary Game Theory (original) (raw)

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