Particle Swarm Optimisation (PSO) is a metaheuristic used to solve search tasks and is inspired by the flocking behaviour of birds. Traditionally careful tuning of parameters are required to avoid stagnation. Many animals forage using search strategies that show power law distributions in their motions in the form of Lévy flight random walks. It might be expected that when exploring spaces for ...

Most of the real world science and engineering optimisation problems are non-linear and constrained. This paper presents a hybrid algorithm by integrating particle swarm optimisation with stochastic ranking for solving standard constrained numerical and engineering benchmark problems. Stochastic ranking technique that uses bubble sort mechanism for ranking the solutions and maintains a balance ...

This study examines a variable-length Particle Swarm Algorithm for Social Programming. The Grammatical Swarm algorithm is a form of Social Programming as it uses Particle Swarm Optimisation, a social swarm algorithm, for the automatic generation of programs. This study extends earlier work on a fixed-length incarnation of Grammatical Swarm, where each individual particle represents choices of p...

The conventional parameter optimisation of PID controller is easy to produce surge and big overshoot, and therefore heuristics such as genetic algorithm (GA), particle swarm optimisation (PSO) are employed to enhance the capability of traditional techniques. But the major problem of these algorithms is that they may be trapped in the local optima of the objective and lead to poor performance. I...

Particle Swarm Optimisation (PSO) is a powerful algorithm for space search problems such as parametric optimisation. Particles with Lévy-Flights have a long-tailed probability of outlier jumps in the problem space that provide a good compromise between local space exploration and local minima avoidance. Generating many particles and their trajectories with Lévy-random deviates is computationall...

This work introduces a generalised hybridisation strategy which utilises the information sharing mechanism deployed in Stochastic Di usion Search when applied to a number of population-based algorithms, e ectively merging this nature-inspired algorithm with some population-based algorithms. The results reported herein demonstrate that the hybrid algorithm, exploiting information-sharing within ...

The set-based particle swarm optimisation algorithm is a swarm-based meta-heuristic that has gained popularity in recent years. In contrast to the original algorithm, used solve discrete and combinatorial problems. main objective of this paper review provide an overview problems which been applied. This starts with examination previous attempts create discusses shortcomings existing attempts. e...

In engineering field, many problems are hard to solve in some definite interval of time. These problems known as “combinatorial optimisation problems” are of the category NP. These problems are easy to solve in some polynomial time when input size is small but as input size grows problems become toughest to solve in some definite interval of time. Long known conventional methods are not able to...