This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and optimization. Whereas optimization has been popular academic topic for decades, swarm intelligence is relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed i...

A cognitive collaborative multi-agent control architecture that addresses real-world control problems for swarms of mobile robots is proposed. The swarm's emergent behaviour is obtained by using a distributed Particle Swarm Optimization inspired algorithm. A swarm-user interface is also presented and offers a way for a human operator to interact with and guide the robotic swarm without limiting...

As the computer technology improves rapidly, the scale of software has increased greatly, which makes it more and more difficult to find a bug in software. As a result, the enhancement of software quality and reliability has become an important task in the field of software engineering. Test is an important step that guarantees software quality and reliability. We put forward a novel multi-obje...

This paper focuses on solving a typical multi-mode resource leveling problem, in which activity duration depends on committed resources, project deadlines and other constraints. To solve this problem, we establish a multi-objective model to minimize project duration, resource requirements and resource variance. Based on the established model, a novel Elitist Vector Evaluated Particle Swarm Opti...

by MICHAEL ANTHONY KOVACINA This work implements and demonstrates a set of tools to automatically generate swarm algorithms. SWEEP, SWarm Experimentation and Evaluation Platform, a swarm algorithm development and simulation platform is implemented. To demonstrate the utility of SWEEP, several swarm-based algorithms are constructed for free-moving and dynamics-constrained agents, culminating in ...

A distributed variant of multi-objective particle swarm optimization called multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) was used to develop a new optimization-based synthesis routine for Grashof mechanisms. By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing objective functions is removed and the ...

In this paper, a multi-objective particle swarm optimization algorithm with a new global best (gbest) selection strategy is proposed for dealing with multi-objective problems. In multi-objective particle swarm optimization, gbest plays an important role in convergence and diversity of solutions. A K-means algorithm and proportional distribution based approach is used to select gbest from the ar...