Particle swarm optimisation (PSO) is a stochastic optimisation algorithm based on swarm intelligence. The algorithm applies the concept of social interaction to find optimal solution. Sina Weibo is one of the most popular Chinese microblog platforms. Microblog users participate in network interaction by publishing tweets and retweets. The influences of microblog users are determined by the user...

Agent swarm optimisation (ASO) is a new paradigm based on particle swarm optimisation that exploits distributed or swarm intelligence and borrows some ideas from multi-agent based systems. It is aimed at supporting decision-making processes by solving either single or multi-objective optimisation problems. Classical methods of optimisation have been shown to be poorly suited for many real-world...

Particle Swarm Optimisation (PSO) makes use of a dynamical system for solving a search task. Instead of adding search biases in order to improve performance in certain problems, we aim to remove algorithm-induced scales by controlling the swarm with a mechanism that is scale-free except possibly for a suppression of scales beyond the system size. In this way a very promising performance is achi...

Ontologies describe real-world entities in terms of axioms, i.e. statements about them, and have become an established instrument for formally modelling and representing knowledge. The diversity of available ontologies results in a heterogeneous landscape where ontologies can overlap in their content. Such an overlap can be caused by ontologies modelling the same or similar domains created by d...

Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaini...

The ‘bare bones’ (BB) formulation of particle swarm optimisation (PSO) was originally advanced as a model of PSO dynamics. The idea was to model the forces between particles with sampling from a probability distribution in the hope of understanding swarm behaviour with a conceptually simpler particle update rule. ‘Bare bones with jumps’ (BBJ) proposes three significant extensions to the BB algo...

Cuckoo search (CS) is one of the most recent population-based meta-heuristics. CS algorithm is based on the cuckoo’s behaviour and the mechanism of Lévy flights. Unfortunately, the standard CS algorithm is proposed only for continuous optimisation problems. In this paper, we propose a discrete binary cuckoo search (BCS) algorithm in order to deal with binary optimisation problems. To get binary...