Particle swarm optimization with spatially meaningful neighbours

Lane, J.; Engelbrecht, Andries P.; Gain, James

doi:10.1109/sis.2008.4668281

Cited by 41 publications

(20 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to Lane et al (2008), particle swarm optimization is a powerful, yet simple population based optimization strategy, particularly well suited for finding extrema in continuous non-linear functions. The approach is derived in part from the interesting way flocks of birds and swarms in nature search for food.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

Comparison Using Particle Swarm Optimization and Genetic Algorithm for Timetable Scheduling

Adrianto¹

2014

Journal of Computer Science

View full text Add to dashboard Cite

Lecturer timetable scheduling is an important part in the resource allocation planning. Due to the large amount of transactions and various related constraints have to be taken into account in timetable scheduling process, resource manager team shall need a lot of time to the solve the problem. This research is aimed to discuss the application of Particle Swarm Optimization (PSO) that can be used to automatically generate optimal lecturer timetable scheduling. Using Software Laboratory Center (SLC) data, some hard constraints are taken into account such as the assistant should teach according to their qualifications, teaching in their work shift and doesn't teach any course that are being taken. Some soft constraints are also considered and the associated cost function is built based on these hard and soft constraints. Based on the computational results, the amount of penalty obtained by the PSO is much smaller than the GA on 500th iteration. The calculation is performed by comparing the amount of penalty that earned each time a hard constraint or soft constraint is violated by the implementation of PSO or GA to the total penalty obtained when all constraints are violated.

show abstract

Section: Metaheuristic Methodsmentioning

confidence: 99%

Comparison Using Particle Swarm Optimization and Genetic Algorithm for Timetable Scheduling

Adrianto¹

2014

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…The lower and upper bounds of the scaling factor are 0.2 and 0.8, respectively, and the crossover probability 0.2. For PSO, a static topology [43] and inertia weight method to update particles' positions and velocities [44] are used. The inertia weight is 1, inertia weight damping ration 0.99, personal learning coefficient 1.5 and global learning coefficient 2.0.…”

Section: Weights Combination Identifiermentioning

confidence: 99%

Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

Abdelbari

Shafi

2017

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

Conceptual or explanatory models are a key element in the process of complex system modelling. They not only provide an intuitive way for modellers to comprehend and scope the complex phenomena under investigation through an abstract representation but also pave the way for the later development of detailed and higher-resolution simulation models. An evolutionary echo state network-based method for supporting the development of such models, which can help to expedite the generation of alternative models for explaining the underlying phenomena and potentially reduce the manual effort required, is proposed. It relies on a customised echo state neural network for learning sparse conceptual model representations from the observed data. In this paper, three evolutionary algorithms, a genetic algorithm, differential evolution and particle swarm optimisation are applied to optimize the network design in order to improve model learning. The proposed methodology is tested on four examples of problems that represent complex system models in the economic, ecological and physical domains. The empirical analysis shows that the proposed technique can learn models which are both sparse and effective for generating the output that matches the observed behaviour.

show abstract

“…Since the value of the inertia coefficient c ⋆ 0 is less than 1.0, the CPSO has better convergence compared to the original PSO. Consequently, it is usually applied for solving many practice problems as the best parameter values to search (17).…”

Section: The Psomentioning

confidence: 99%

The Pursuit of Evolutionary Particle Swarm Optimization

Zhang¹

2012

Theory and New Applications of Swarm Intelligence

View full text Add to dashboard Cite

IntroductionParticle swarm optimization inspired with the social behavior in flocks of birds and schools of fish is an adaptive, stochastic and population-based optimization technique which was created by Kennedy and Eberhart in 1995 (9; 12). As one of the representatives of swarm intelligence (20), it has the distinctive characteristics: information exchange, intrinsic memory, and directional search in contrast to genetic algorithms (GAs) (14) and genetic programming (GP) (16). Due to ease of understanding and implementation, good expression and expandability, higher searching ability and solution accuracy, the technique has been successfully applied to different fields of science, technology, engineering, and applications for dealing with various large-scale, high-grade nonlinear, and multimodal optimization problems (22; 23).Although the mechanism of a plain particle swarm optimizer (the PSO) (13) is simple to implement with only a few parameters, in general, it can provide better computational results in contrast to other methods such as machine learning, neural network learning, genetic algorithms, tabu search, and simulated annealing (1). Nevertheless, like other optimization methods, an essential issue is how to make the PSO efficiently in dealing with different kinds of optimization problems. And it is well-known that the systematic selection of the parameter values in the PSO is one of fundamental manners to the end, and the most important especially for establishing a policy which determines the PSO with high search performance.However, in fact how to properly determine the values of parameters in the PSO is a quite attractive but hard subject especially for a detailed analysis of higher order (7). The cause is because the search behavior of the PSO has very high indeterminacy. Usually, these parameter values related to internal stochastic factors need to be adjusted for keeping search efficiency (5).As new development and expansion of the technique of meta-optimization 1 , the above issue already can be settled by the method of evolutionary particle swarm optimization (EPSO) (27), which provides a good framework to systematically estimate appropriate values of 1 Meta-optimization, in general, is defined as the process of using an optimization algorithm to automatically search the best optimizer from all computable optimizers. 4www.intechopen.com

show abstract

Particle swarm optimization with spatially meaningful neighbours

Cited by 41 publications

References 18 publications

Comparison Using Particle Swarm Optimization and Genetic Algorithm for Timetable Scheduling

Comparison Using Particle Swarm Optimization and Genetic Algorithm for Timetable Scheduling

Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

The Pursuit of Evolutionary Particle Swarm Optimization

Contact Info

Product

Resources

About