Journal ArticleDOI
Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm
Reads0
Chats0
TLDR
A global study of the behavior of TRIBES under several conditions is performed in order to determine strengths and drawbacks of this adaptive PSO algorithm.Abstract:
This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.read more
Citations
More filters
Journal ArticleDOI
Book review: particle swarm optimization for single objective continuous space problems: A review
TL;DR: This paper reviews recent studies on the Particle Swarm Optimization (PSO) algorithm and presents some potential areas for future study.
Journal ArticleDOI
A Self-Learning Particle Swarm Optimizer for Global Optimization Problems
TL;DR: A novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems, which can enable a particle to choose the optimal strategy according to its own local fitness landscape.
Journal ArticleDOI
Incremental Social Learning in Particle Swarms
TL;DR: This paper derives analytically the probability density function induced by the proposed initialization rule applied to new particles and compares the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms and a random restart local search algorithm.
Journal ArticleDOI
A model-independent Particle Swarm Optimisation software for model calibration
TL;DR: Flexibility of the hydroPSO package suggests it can be implemented in a wider range of models requiring some form of parameter optimisation, and is effective and efficient compared to commonly used optimisation algorithms.
Journal ArticleDOI
Multi-objective dynamic economic emission dispatch using particle swarm optimisation variants
TL;DR: The results show that the PSO AWL outperforms the SPSO for every topology implemented and is compared to state of the art genetic algorithm (NSGA-II) and multi-agent eeinforcement learning (MARL).
References
More filters
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
BookDOI
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
TL;DR: Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways.
Journal ArticleDOI
Ant system: optimization by a colony of cooperating agents
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.