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In this article we approach the problem of distributed agreement in multi-agent systems using asynchronous particle swarm optimization (PSO) with dynamic neighborhood. The agents are considered as PSO particles which are assumed to have time-dependent neighborhoods, operate asynchronously and incur time delays during information exchange. The performance of the PSO based agreement algorithm is verified...
The importance of any inferences that can be taken from underlying genetic networks of observed time-series data of gene expression patterns should not be overlooked. They are one of the largest topics within bioinformatics. The S-system model is one good choice for analyzing such genetic networks due to the fact that it can capture various dynamics. One problem this model faces is the fact that the...
Particle swarm optimisation (PSO) algorithms have been successfully used to solve many complex real-world optimisation problems. Since their introduction in 1995, the focus of research in PSOs has largely been on the algorithmic side with many new variations proposed on the original PSO algorithm. Relatively little attention has been paid to the study of problems with respect to PSO performance. The...
Recently, high levels of energy consumption in datacenters has become a concern not only due to operational costs, but also due to adverse effects on the environment (i.e., carbon emission, climate change, etc.) Virtualization technology can provide better management of physical servers/machines (PM) and may help reduce power consumption. The purpose of this study is to minimize the total energy consumption...
In view of the existing polygonal approximation algorithm of digital curves can't effectively solve the problem of polygonal approximation constrained by the offset direction, this paper proposes an algorithm of polygonal approximation constrained by the offset direction. First, the offset polygon of the original digital curve is calculated under the control of offset direction and distance. Second,...
A new variant of the competitive coevolutionary team-based particle swarm optimiser (CCPSO(t)) algorithm is developed to train multi-agent teams from zero knowledge. Analysis show that the CCPSO algorithm stagnates during the training of simple soccer players. It is hypothesised that the stagnation is caused by saturation of the neural network weights. The CCPSO(t) algorithm is developed to overcome...
Real-time navigation and mapping of an autonomous robot is one of the major challenges in intelligent robot systems. In this paper, a novel sensor-based biologically inspired neural network algorithm to real-time collision-free navigation and mapping of an autonomous mobile robot in a completely unknown environment is proposed. A local map composed of square grids is built up through the proposed...
Particle swarm optimization (PSO) algorithms have a number of parameters to which their behaviour is sensitive. In order to avoid problem-specific parameter tuning, a number of self-adaptive PSO algorithms have been proposed over the past few years. This paper compares the behaviour and performance of a selection of self-adaptive PSO algorithms to that of time-variant algorithms on a suite of 22 boundary...
Applying weight regularisation to gradient-descent based neural network training methods such as backpropagation was shown to improve the generalisation performance of a neural network. However, the existing applications of weight regularisation to particle swarm optimisation are very limited, despite being promising. This paper proposes adding a regularisation penalty term to the objective function...
Cuckoo search is a swarm-intelligence-based algorithm that is very effective for solving highly nonlinear optimization problems. In this paper, the multiobjective cuckoo search is extended so as to obtain high-quality Pareto fronts more accurately for multiobjective optimization problems with complex constraints. The proposed approach uses a combination of the cuckoo search with non-dominated sorting...
We compare three approaches to solving digital jigsaw puzzles with wrap-around connections: human-only, swarm-only, and a hybrid approach that requires humans to interact with the swarm in a high-level, scalable manner. Using an iterative improvement strategy, some positive aspects of the human solvers migrate to the swarm-only approach. As the swarm-only approach gets better, humans continue to assist...
Multi-Objective Problems (MOPs) presents two or more objective functions to be simultaneously optimized. MOPs presenting more than three objective functions are called Many-Objective Problems (MaOPs) and pose challenges to optimization algorithms. Multi-objective Particle Swarm Optimization (MOPSO) is a promising meta-heuristic to solve MaOPs. Previous works have proposed different leader selection...
MAX-SAT is a classic NP-hard optimization problem. Many real problems can be easily represented in, or reduced to MAX-SAT, and thus it has many applications. Finding optimum solutions of NP-hard optimization problems using limited computational resources seems infeasible in general. In particular, all known exact algorithms for MAX-SAT require worst-case exponential time, so evolutionary algorithms...
In this paper we propose a fuzzy system for parameter adaptation in ant colony optimization (ACO). ACO is a method inspired in the behavior of ant colonies to find food and its objective are discrete optimization problems. We developed various fuzzy systems for parameter adaptation and in this paper a comparison was made between them. The use of a fuzzy system is to control the diversity of the solutions,...
In this paper, a novel PSO based metaheuristic is proposed. This described approach is inspired by human gathering mechanisms. Each particle is given a possibility to follow a randomly selected particle from the swarm. When a promising search area is found by the particle, it remains stationary for a given number of iterations improving the chances of other particles following such a stationary particle...
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