Lifecycle-Based Swarm Optimization Method for Numerical Optimization

Lifecycle-based Swarm Optimization Method for Constrained Optimization

Each biologic must go through a process from birth, growth, reproduction until death, this process known as life cycle. This paper borrows the biologic life cycle theory to propose a Lifecycle-based Swarm Optimization (LSO) algorithm. Based on some features of life cycle, LSO designs six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In this...

Taguchi-Particle Swarm Optimization for Numerical Optimization

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the ...

Numerical Integration Method Based on Particle Swarm Optimization

In this paper, a novel numerical double integration method based on Particle Swarm Optimization (PSO) was presented. PSO is a technique based on the cooperation between particles. The exchange of information between these particles allows to resolve difficult problems. This approach is carefully handled and tested with an illustrated example.

SWAF: Swarm Algorithm Framework for Numerical Optimization

A swarm algorithm framework (SWAF), realized by agent-based modeling, is presented to solve numerical optimization problems. Each agent is a bare bones cognitive architecture, which learns knowledge by appropriately deploying a set of simple rules in fast and frugal heuristics. Two essential categories of rules, the generate-and-test and the problem-formulation rules, are implemented, and both ...

An Idea Based on Honey Bee Swarm for Numerical Optimization