Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.

Measuring exploration/exploitation in particle swarms using swarm diversity

Abstract: An important factor contributing to the success of particle swarm optimization (PSO) is the balance between exploration and exploitation of the swarm. Exploration is typically preferred at the initial stages of the search but is required to gradually give way to exploitation of promising solutions as the search progresses. The diversity of a particle swarm optimization algorithm can be defined, simply, as the degree of dispersion of the particles in the swarm. This dispersion could be defined around some center-point or not. It could also be defined based on the positions of the particles or on their velocities. This paper takes a look at some of the different definitions of swarm diversity with the intention of determining their usefulness in quantifying swarm exploration/exploitation. This work is intended to lay the foundations for the development of a suitable means to quantify the rate of change from exploration to exploitation of a PSO, i.e. the rate of change of diversity.

An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training

Abstract: Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for continuous optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm.

The Wind Driven Optimization Technique and its Application in Electromagnetics

Abstract: A new type of nature-inspired global optimization methodology based on atmospheric motion is introduced. The proposed Wind Driven Optimization (WDO) technique is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-modal problems with the potential to implement constraints on the search domain. At its core, a population of infinitesimally small air parcels navigates over an $N$ -dimensional search space following Newton's second law of motion, which is also used to describe the motion of air parcels within the earth's atmosphere. Compared to similar particle based algorithms, WDO employs additional terms in the velocity update equation (e.g., gravitation and Coriolis forces), providing robustness and extra degrees of freedom to fine tune. Along with the theory and terminology of WDO, a numerical study for tuning the WDO parameters is presented. WDO is further applied to three electromagnetics optimization problems, including the synthesis of a linear antenna array, a double-sided artificial magnetic conductor for WiFi applications, and an E-shaped microstrip patch antenna. These examples suggest that WDO can, in some cases, out-perform other well-known techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) or Differential Evolution (DE) and that WDO is well-suited for problems with both discrete and continuous-valued parameters.