The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorpora...

We reconsider stochastic convergence analyses of particle swarm optimisation, and point out that previously obtained parameter conditions are not always sufficient to guarantee mean square convergence to a local optimum. We show that stagnation can in fact occur for non-trivial configurations in non-optimal parts of the search space, even for simple functions like Sphere. The convergence proper...

This paper investigates the use of evolutionary optimisation techniques to register a template with a scene image. An error function is created to measure the correspondence of the template to the image. The problem presented here is to optimise the horizontal, vertical and scaling parameters that register the template with the scene. The Genetic Algorithm, Simulated Annealing and Particle Swar...

In this paper we describe a number of global optimisation problems connected to spacecraft trajectory design. Each problem is coded in the form of a blackbox objective function accepting, as inputs, the decision vector and returning the objective function and the constraint evaluation. The code is made available on line as a challenge to the community to develop performing algorithms able to so...

A novel feedback control method for robotic manipulators with random communication delays by combining the optimal P-type iterative learning control (ILC) idea with a minimum tracking error entropy control strategy is presented. The control design is formulated as an optimisation problem with a proper performance index and a constraint. In specific, the performance index implies the idea of the...

Alloy design is a challenging multi-objective optimisation problem, which consists of finding the optimal processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of steels. In this paper, we combine fuzzy modelling and Particle Swarm Optimisation (PSO) to address the multi-objective optimal alloy design problem. An adaptive weighted...

Network planning in wireless communication systems, which has strong impacts on the system performance, is a complex and challenging issue. This paper investigates the network planning problem under high-speed railway communication scenarios. A system model is characterised in terms of distributed antenna technology. An optimisation problem is formulated to optimise the total system cost with t...

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells ...

A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants – Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells ...

Purpose: This paper presents an efficient and reliable swarm intelligence-based approach, namely particle swarm optimization [PSO] technique, to optimize the hardness and the parameters that affect the hardness in the Ni-Diamond composite coatings. Design/methodology/approach: Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the i...