A Dynamic Island-Based Genetic Algorithms Framework

This work presents a dynamic island model framework for helping the resolution of combinatorial optimization problems with evolutionary algorithms. In this framework, the possible migrations among islands are represented by a complete graph. The migrations probabilities associated to each edge are dynamically updated with respect to the last migrations impact. This new framework is tested on the well-known 0/1 Knapsack problem and MAX-SAT problem. Good results are obtained and several properties of this framework are studied

Introdução de um índice de desempenho para a migração no modelo de ilhas

In this work, a performance index was added to the island model, to evaluate how efficiently the population of an island is at solving a given problem. The islands with higher indexes receive more individuals when migration occurs. when the migration operator is used, a few individuals are chosen to make a decision: either go to another island or stay in the current island. The individual has a higher chance of choosing an island with a high performance index. After running the simulations, we noticed that the solutions of the new model were as good as the solutions from the best algorithm for each problem. We also noticed that even if we remove the most efficient algorithm from the model, it manages to adapt and still provide efficient solutions, making use of the characteristics from the remaining algorithms.Nesse trabalho é acrescentado ao modelo de ilhas um índice de desempenho para avaliar o quão eficiente a ilha está sendo em resolver problemas de otimização sem restrições. As ilhas com índices maiores recebem mais indivíduos quando a migração ocorre. Na ocasião da aplicação do operador de migração, são selecionados alguns indivíduos para migrarem de uma determinada ilha. Esses indivíduos escolhem então suas ilhas de destino, ou se permanecem na ilha em que estão, com mais chances de escolher uma ilha que possui um alto índice de desempenho. As simulações realizadas indicam que o modelo proposto apresenta resultados semelhantes aos resultados gerados pelo melhor algoritmo indicado pela literatura para cada problema. Percebeu-se também que ao retirar o algoritmo mais eficiente do modelo, o modelo proposto consegue se adaptar e gerar solu¸c˜oes eficientes, utilizando-se das caracter´ısticas dos algoritmos restantes.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms

In this paper we present a novel tool, using both OpenMP and MPI protocols, for optimising the efficiency of Urban Transportation Systems within a defined catchment, town or city. We build on a previously presented model which uses a Genetic Algorithm with novel genetic operators to optimise route sets and provide a transport network for a given problem set. This model is then implemented within a Parallel Multi-Objective Genetic Algorithm and demonstrated to be scalable to within the scope of real world, [city-wide], problems. This paper compares and contrasts three methods of parallel distribution of the Genetic Algorithm's computational workload: a job farming algorithm and two variations on an ‘Islands’ approach. Results are presented in the paper from both single and mixed mode strategies. The results presented are from a range of previously published academic problem sets. Additionally a real world inspired problem set is evaluated and a visualisation of the optimised output is given

Parallel transfer evolution algorithm

Parallelization of an evolutionary algorithm takes the advantage of modular population division and information exchange among multiple processors. However, existing parallel evolutionary algorithms are rather ad hoc and lack a capability of adapting to diverse problems. To accommodate a wider range of problems and to reduce algorithm design costs, this paper develops a parallel transfer evolution algorithm. It is based on the island-model of parallel evolutionary algorithm and, for improving performance, transfers both the connections and the evolutionary operators from one sub-population pair to another adaptively. Needing no extra upper selection strategy, each sub-population is able to select autonomously evolutionary operators and local search operators as subroutines according to both the sub-population's own and the connected neighbor's ranking boards. The parallel transfer evolution is tested on two typical combinatorial optimization problems in comparison with six existing ad-hoc evolutionary algorithms, and is also applied to a real-world case study in comparison with five typical parallel evolutionary algorithms. The tests show that the proposed scheme and the resultant PEA offer high flexibility in dealing with a wider range of combinatorial optimization problems without algorithmic modification or redesign. Both the topological transfer and the algorithmic transfer are seen applicable not only to combinatorial optimization problems, but also to non-permutated complex problems

Dynamic multi-objective optimization using evolutionary algorithms

Dynamic Multi-objective Optimization Problems (DMOPs) offer an opportunity to examine and solve challenging real world scenarios where trade-off solutions between conflicting objectives change over time. Definition of benchmark problems allows modelling of industry scenarios across transport, power and communications networks, manufacturing and logistics. Recently, significant progress has been made in the variety and complexity of DMOP benchmarks and the incorporation of realistic dynamic characteristics. However, significant gaps still exist in standardised methodology for DMOPs, specific problem domain examples and in the understanding of the impacts and explanations of dynamic characteristics. This thesis provides major contributions on these three topics within evolutionary dynamic multi-objective optimization. Firstly, experimental protocols for DMOPs are varied. This limits the applicability and relevance of results produced and conclusions made in the field. A major source of the inconsistency lies in the parameters used to define specific problem instances being examined. The uninformed selection of these has historically held back understanding of their impacts and standardisation in experimental approach to these parameters in the multi-objective problem domain. Using the frequency and severity (or magnitude) of change events, a more informed approach to DMOP experimentation is conceptualized, implemented and evaluated. Establishment of a baseline performance expectation across a comprehensive range of dynamic instances for well-studied DMOP benchmarks is analyzed. To maximize relevance, these profiles are composed from the performance of evolutionary algorithms commonly used for baseline comparisons and those with simple dynamic responses. Comparison and contrast with the coverage of parameter combinations in the sampled literature highlights the importance of these contributions. Secondly, the provision of useful and realistic DMOPs in the combinatorial domain is limited in previous literature. A novel dynamic benchmark problem is presented by the extension of the Travelling Thief Problem (TTP) to include a variety of realistic and contextually justified dynamic changes. Investigation of problem information exploitation and it's potential application as a dynamic response is a key output of these results and context is provided through comparison to results obtained by adapting existing TTP heuristics. Observation driven iterative development prompted the investigation of multi-population island model strategies, together with improvements in the approaches to accurately describe and compare the performance of algorithm models for DMOPs, a contribution which is applicable beyond the dynamic TTP. Thirdly, the purpose of DMOPs is to reconstruct realistic scenarios, or features from them, to allow for experimentation and development of better optimization algorithms. However, numerous important characteristics from real systems still require implementation and will drive research and development of algorithms and mechanisms to handle these industrially relevant problem classes. The novel challenges associated with these implementations are significant and diverse, even for a simple development such as consideration of DMOPs with multiple time dependencies. Real world systems with dynamics are likely to contain multiple temporally changing aspects, particularly in energy and transport domains. Problems with more than one dynamic problem component allow for asynchronous changes and a differing severity between components that leads to an explosion in the size of the possible dynamic instance space. Both continuous and combinatorial problem domains require structured investigation into the best practices for experimental design, algorithm application and performance measurement, comparison and visualization. Highlighting the challenges, the key requirements for effective progress and recommendations on experimentation are explored here