Geometric particle swarm optimisation on binary and real spaces

Genetic Programming, 2007

Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms. This connection enables us to generalize PSO to virtually any solution representation in a natural and straightforward way. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces.

Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. The new geometric PSO (GPSO) applies naturally to both continuous and combinatorial spaces. We demonstrate this for the cases of Euclidean, Manhattan, and Hamming spaces and report extensive experimental results. We also demonstrate the applicability of GPSO to more challenging combinatorial spaces. The Sudoku puzzle is a perfect candidate to test new algorithmic ideas because it is entertaining and instructive as well as being a nontrivial constrained combinatorial problem. We apply GPSO to solve the Sudoku puzzle.

Journal of Artificial Evolution and Applications, 2008

Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimisation (PSO) and evolutionary algorithms. This connection enables us to generalise PSO to virtually any solution representation in a natural and straightforward way. The new geometric PSO (GPSO) applies naturally to both continuous and combinatorial spaces. We demonstrate this for the cases of Euclidean, Manhattan, and Hamming spaces and report extensive experimental results. We also demonstrate the applicability of GPSO to more challenging combinatorial spaces. The Sudoku puzzle is a perfect candidate to test new algorithmic ideas because it is entertaining and instructive as well as being a nontrivial constrained combinatorial problem. We apply GPSO to solve the Sudoku puzzle.

International Journal of Computer Applications, 2022

The ultimate performance of particle swarm optimization is influenced by hyper-parameters like the inertia, cognitive and social coefficient values. These hyper-parameters have a significant effect on search capability of the particle swarm optimization. When looking at previous studies that are carried out to calculate these coefficients, none of these studies has been inspired by geometric techniques to illustrate for the influence of these components on best position realization. In this, article a geometric approach to how the allocation of social, cognitive and inertia regions on a search space enables particles to move to their best positions at every iteration time. In experiment and benchmark tests, the study validates the applicability of the proposed approach to classification problem using EMNIST dataset. The modified PSO approach gives successful results in separating data into appropriate classes which confirms that the proposed method is highly competitive in guiding the directional movement of the particles towards the best positions.

Particle swarm optimization (or swarm intelligence) for combinatorial optimization BY: NAME: ROHIT JHA MATRIC NO: 078252F03 COMBINATORIAL OPTIMIZATION Combinatorial optimization [1] is a topic in theoretical computer science and applied mathematics that consists of finding the least-cost solution to a mathematical problem in which each solution is associated with a numerical cost. In many such problems, exhaustive search is not feasible. It operates on the domain of those optimization problems, in which the set of feasible solutions is discrete or can be reduced to discrete, and in which the goal is to find the best solution [2]. Some common problems involving combinatorial optimization are the Travelling Salesman Problem (TSP) and the Minimum Spanning Tree (MST)

Control & Automation, …, 2007

Particle Swarm Optimization, 2009

The Particle Swarm Optimization (PSO) algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1995, and since then has been utilized as a powerful optimization tool in a wide range of applications. In this paper, a general picture of the research in PSO is presented based on a comprehensive survey of about 1800 PSO-related papers published from 1995 to 2008. After a brief introduction to the PSO algorithm, a new taxonomy of PSO-based methods is presented. Also, 95 major PSO-based methods are introduced and their parameters summarized in a comparative table. Finally, a timeline of PSO applications is portrayed which is categorized into 8 main fields.

Swarm Intelligence, 2007

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.

2009

Abstract Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO is mostly based on the gbest (global best) particle topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes.