A Novel Ranking-based Optimal Guides Selection Strategy in MOPSO

© 2016 Published by Elsevier B.V. A challenging issue with multi-objective particle swarm optimization (MOPSO) is the mechanism to select the optimal guides. This paper presents a new strategy based on ranking dominance and integrates into MOPSO. By using the ranking information and incorporating the chebychev distance of particle in objective space, we implement the selection of gbest and pbest simply and elegantly. On the basis of ranking, we propose a new maintenance strategy for updating the external archive which can obtain a more diverse and uniform distribution. Furthermore, a qualitative and quantitative analysis in terms of convergence analysis over some benchmarks is presented, providing a basis for conclusions about the proposed method. showing that the proposed method performs better than the adopted algorithms

Visualisation and ordering of many-objective populations

Copyright © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We introduce novel methods of visualising and ordering multi-and many-objective populations. We compare individuals by the probability that one will beat another in a tournament on a randomly selected objective. This defines a weighted directed graph representing the population. We introduce a novel graphical representation of the many objective population based on Pareto shells. We examine leagues, Pareto shells, preference ordering, average rank, outflow, the stationary distribution and the power index for ordering the population finding that the average rank is equivalent to outflow and that these together with the power index are generally superior. Finally, we show how to seriate objectives to enhance the interpretability of heatmap visualisations

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Effective and Efficient Evolutionary Many-Objective Optimization

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonMany-objective optimization is core to both artificial intelligence and data analytics as real-world problems commonly involve multiple objectives which are required to be optimized simultaneously. A large number of evolutionary algorithms have been developed to search for a set of Pareto optimal solutions for many-objective optimization problems. It is very rare that a many-objective evolutionary algorithm performs well in terms of both effectiveness and efficiency, two key evaluation criteria. Some algorithms may struggle to guide the solutions towards the Pareto front, e.g., Pareto-based algorithms, while other algorithms may have difficulty in diversifying the solutions evenly over the front on certain problems, e.g., decomposition-based algorithms. Furthermore, some effective algorithms may become very computationally expensive as the number of objectives increases, e.g., indicator-based algorithms. The aim of this thesis is to investigate how to make evolutionary algorithms perform well in terms of effectiveness and efficiency in many-objective optimization. After conducting a review of key concepts and the state of the art in the evolutionary many-objective optimization, this thesis shows how to improve the effectiveness of conventional Pareto-based algorithms on a challenging real-world problem in software engineering. This thesis then explores how to further enhance the effectiveness of leading many-objective evolutionary algorithms in general by extending the capability of a very popular and widely cited bi-goal evolution method. Last but not least, this thesis investigates how to strike a balance between effectiveness and efficiency of evolutionary algorithms when solving many-objective optimization problems. The work reported is based on either real-world or recognized synthetic datasets, and the proposed algorithms are compared and evaluated against leading algorithms in the field. The work does not only demonstrate ways of improving the effectiveness and efficiency of many-objective optimization algorithms but also led to promising areas for future research

Novas estratégias para otimização por nuvem de partículas aplicadas a problemas com muitos objetivos

Orientadora: Profa. Dra. Aurora Trinidad Ramirez PozoTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Curso de Pós-Graduação em Informática. Defesa: Curitiba, 14/03/2013Bibliografia: fls.206-218Resumo: Problemas de otimização multiobjetivo possuem mais de uma função objetivo que estão em conflito. Devido a essa característica, nao existe somente uma melhor soluçao, mas sim um conjunto com as melhores soluções do problema, definidas pelos conceitos da teoria da Otimalidade de Pareto. Algoritmos Evolucionarios Multiobjetivo sao aplicados com sucesso em diversos Problemas de Otimizacão Multiobjetivo. Dentre esses algoritmos, os baseados na Otimizaçao por Nuvem de Partículas Multiobjetivo (MOPSO) apresentam bons resultados para problemas multiobjetivo e se destacam por possuirem características específicas, como a cooperaçao entre as partículas da populacao. Porem, quando o numero de funcoes objetivo cresce, os algoritmos evolucionários multiobjetivo baseados em dominancia de Pareto encontram algumas dificuldades em definir quais sao as melhores solucoes e nao efetuam uma busca que converge para as soluçães ótimas do problema. A Otimizaçao com muitos objetivos e uma area nova que visa propor novos metodos para reduzir a deterioracao da busca desses algoritmos em problemas de otimizacão com muitos objetivos (problemas com mais de três funcoes objetivo). Assim, motivado por esse campo de pesquisa ainda em aberto e pelo fato da meta-heurística MOPSO ser pouco utilizada na Otimizaçao com Muitos Objetivos, este trabalho de doutorado contribuí com a proposta de novas metodos e algoritmos que buscam explorar três diferentes aspectos da Otimizacao por Nuvem de Partículas Multiobjetivo: uso de novas relacoes de preferencias, metodos de arquivamento e algoritmos MOPSO com multiplos enxames. Neste estudo, íe feita uma aníalise empírica que utiliza um conjunto de indicadores de qualidade e problemas de benchmark com o intuito de analisar aspectos como convergencia e diversidade da busca dos algoritmos utilizados. Por fim, esta tese traca os principais caminhos que serãao seguidos nos trabalhos futuros.Abstract: Multiobjective Optimization Problems have more than one objective function that are often in conflict. Therefore, there is no single best solution, but a set of the best solutions defined by the concepts of Pareto Optimality theory. Multiobjective Evolutionary Algorithms are applied successfully in several Multiobjective Optimization Problems. Among these algorithms, we highlight those based on Multiobjective Particle Swarm Optimization (MOPSO), since they have good results for multiobjective problems and exhibit unique characteristics such as cooperation among individuals of the population. However, Multi- Objective Evolutionary Algorithms scale poorly when the number of objectives increases. Many-Objective Optimization Problems are problems that have more than three objective functions. Many-Objective Optimization is a new area, which aims to propose new methods to reduce the deterioration of these algorithms. Thus, motivated by this research field still open and the fact that MOPSO algorithms are still underused in Many-Objective Optimization, this work aims to propose new methods for MOPSO metaheuristic applied to this context. The main contribution of this PhD work is the proposal of new methods and algorithms that seek to explore three different aspects of MOPSO metaheuristic: the use of new preference relations, exploring methods of archiving and exploring multi-swarm algorithms. Another important feature presented in this thesis are the empirical analyzes used to validate all new techniques. In this study, we use a set of quality indicators and benchmark problems in order to analyze aspects such as convergence and diversity of the search. Finally, this thesis outlines the main paths that will be followed in future work