Particle Swarm Optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-Organized Criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.

Particle swarm optimisation (PSO) was born just over ten years ago. The initial ideas on particle swarms of Kennedy and Eberhart were aimed at producing computational intelligence by exploiting simple analogues of social interaction, rather than purely individual cognitive abilities. The first simulations [1] were influenced by Heppner’s and Grenander’s work [2] and involved analogues of bird f...

In this paper, we present two optimisation methods for a generic boids swarm model which is derived from the original Reynolds’ boids model to simulate the aggregate moving of a fish school. The aggregate motion is the result of the interaction of the relatively simple behaviours of the individual simulated boids. The aggregate moving vector is a linear combination of every simple behaviour rul...

Asset allocation is critical for the portfolio management process. In this paper, we solve a dynamic asset allocation problem through a multiperiod stochastic programming model. The objective is to maximise the expected utility of wealth at the end of the planning periods. To improve the optimisation result of the model, we employ swarm intelligent optimisers, the Bacterial Foraging Optimisatio...

This study compares a number of selection regimes for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO). Two distinct gbest selection techniques are shown to exist in the literature, those that do not restrict the selection of archive members and those with ‘distance’ based gbest selection techniques. Theoretic...

This workshop paper addresses Heuristic approaches to solving Sudoku puzzles, with particular focus on Particle Swarm Optimisation(PSO). Sudoku problems and their constraints will be discussed. Heuristics used to solve Sudoku will be identified. We will then propose a component framework for supporting PSO and other Heuristics. Doing so allows us to separately detail aspects of PSO such as init...

Particle Swarm Optimisation (PSO) algorithm is a stochastic search technique, which has exhibited good performance across a wide range of applications. However, very often for multimodal problems involving high dimensions, the algorithm tends to suffer from premature convergence. Analysis of the behaviour of the particle swarm model reveals that such premature convergence is mainly due to the d...