23,480 research outputs found
A multi-objective hyper-heuristic based on choice function
Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an attempt to solve difficult computational optimization problems. We present a learning selection choice function based hyper-heuristic to solve multi-objective optimization problems. This high level approach controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, the proposed hyper-heuristic is applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the hyper-heuristic approach when compared to the performance of each low level heuristic run on its own, as well as being compared to other approaches including an adaptive multi-method search, namely AMALGAM
Choice function based hyper-heuristics for multi-objective optimization
A selection hyper-heuristic is a high level search methodology which operates over a fixed set of low level heuristics. During the iterative search process, a heuristic is selected and applied to a candidate solution in hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems, while work on multi-objective selection hyper-heuristics is limited. This work presents one of the initial studies on selection hyper-heuristics combining a choice function heuristic selection methodology with great deluge and late acceptance as non-deterministic move acceptance methods for multi-objective optimization. A well-known hypervolume metric is integrated into the move acceptance methods to enable the approaches to deal with multi-objective problems. The performance of the proposed hyper-heuristics is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization. Additionally, they are applied to the vehicle crashworthiness design problem as a real-world multi-objective problem. The experimental results demonstrate the effectiveness of the non-deterministic move acceptance, particularly great deluge when used as a component of a choice function based selection hyper-heuristic
An investigation of multi-objective hyper-heuristics for multi-objective optimisation
In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function.
GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems
An investigation of multi-objective hyper-heuristics for multi-objective optimisation
In this thesis, we investigate and develop a number of online learning selection choice function based hyper-heuristic methodologies that attempt to solve multi-objective unconstrained optimisation problems. For the first time, we introduce an online learning selection choice function based hyperheuristic framework for multi-objective optimisation. Our multi-objective hyper-heuristic controls and combines the strengths of three well-known multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which are utilised as the low level heuristics. A choice function selection heuristic acts as a high level strategy which adaptively ranks the performance of those low-level heuristics according to feedback received during the search process, deciding which one to call at each decision point. Four performance measurements are integrated into a ranking scheme which acts as a feedback learning mechanism to provide knowledge of the problem domain to the high level strategy. To the best of our knowledge, for the first time, this thesis investigates the influence of the move acceptance component of selection hyper-heuristics for multi-objective optimisation. Three multi-objective choice function based hyper-heuristics, combined with different move acceptance strategies including All-Moves as a deterministic move acceptance and the Great Deluge Algorithm (GDA) and Late Acceptance (LA) as a nondeterministic move acceptance function.
GDA and LA require a change in the value of a single objective at each step and so a well-known hypervolume metric, referred to as D metric, is proposed for their applicability to the multi-objective optimisation problems. D metric is used as a way of comparing two non-dominated sets with respect to the objective space. The performance of the proposed multi-objective selection choice function based hyper-heuristics is evaluated on the Walking Fish Group (WFG) test suite which is a common benchmark for multi-objective optimisation. Additionally, the proposed approaches are applied to the vehicle crashworthiness design problem, in order to test its effectiveness on a realworld multi-objective problem. The results of both benchmark test problems demonstrate the capability and potential of the multi-objective hyper-heuristic approaches in solving continuous multi-objective optimisation problems. The multi-objective choice function Great Deluge Hyper-Heuristic (HHMO_CF_GDA) turns out to be the best choice for solving these types of problems
Uma hiper-heurÃstica de seleção baseada em decomposição para estabelecer sequências de módulos para o teste de software
Orientador : Prof. Dr. Silvia Regina VergilioDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 03/12/2015Inclui referências : f. 82-88Resumo: Algoritmos multiobjetivos têm sido amplamente utilizados na busca de soluções de diver-sos problemas da computação, e mais especificamente para resolver problemas de Engenharia de Software na area conhecida como SBSE (Search Based Software Engineering). Contudo, conforme são intensificadas as aplicações destes algoritmos, tem-se a dificuldade de determinar qual algoritmo ou quais operadores são os mais indicados para um dado problema. Neste cenário as hiper-heurÃsticas são usadas para que o processo de busca seja guiado de forma que o melhor operador para o problema seja escolhido automaticamente. Neste contexto, destaca-se a hiper-heurÃstica chamada HITO (Hyper-heuristic for the Integration and Test Order Problem), proposta para resolver o problema de estabelecer uma sequencia de módulos para o teste de integração (ITO - Integration and Test Order problem ). Em experimentos, a HITO obteve bons resultados, no entanto, existe a dificuldade para utilizar a HITO em conjunto com algoritmos baseados em decomposto, tais como o MOEA/D e MOEA/D-DRA. Estes algoritmos tem se mostrado bastante competitivos na literatura. Tendo este fato como motivação, este trabalho introduz uma hiper-heurÃstica chamada HITO-DA (Hyper-heuristic for the Integration and Test Order Problem using Decomposition Approach) que propõe uma adaptação na HITO para permitir seu uso com algoritmos baseados em decomposto, na busca de soluções para o problema ITO. A HITO-DA foi instanciada com a meta-heurÃstica MOEA/D-DRA usando o algoritmo de seleção FRRMAB (Fitness Rate Rank Multi Armed Bandit), e um novo algoritmo de seleção FRRCF (Fitness Rate Rank with Choice Function), proposto neste trabalho, que combina caracterÃsticas do FRRMAB e CF (Choice Function). No estudo empÃrico conduzido a HITO-DA obteve melhores resultados do que a meta-heurÃstica MOEA/D em todos os casos, e melhor desempenho em sistemas maiores, quando comparada com a HITO.Abstract: Multi-objective algorithms have been widely applied to find solutions in several problems, more specifically to solve Software Engineering problems, in the field called SBSE (Search Based Software Engineering). However, while these applications are intensified, we find some difficulty to select the most suitable operator for a problem. In this given scenario, hyper-heuristics are used to guide the search process in order to find the most suitable operator for a given problem. In this context, we find a hyper-heuristic, called HITO (Hyper-heuristic for the Integration and Test Order problem), proposed to solve the Integration and Test Order problem (ITO). HITO obtained good results, however, to adapt HITO to work with decomposition based algorithms, such as MOEA/D and MOEA/D-DRA, is a hard task. In the literature, these algorithms have shown competitive results. Based on this motivation, this work introduces a new hyper-heuristic called HITO-DA (Hyper-heuristic for the Integration and Test Order Problem using Decomposition Approach) that adapts HITO to work with decomposition based algorithms and to solve the ITO problem. The HITO-DA was instantiated using the algorithms MOEA/D-DRA, using the selection algorithm FRRMAB (Fitness Rate Rank Multi Armed Bandit) and a new algorithm, introduced in this work, named FRRCF (Fitness Rate Rank with Choice Function). FRRCF combines characteristics of the algorithms FRRMAB and CF (Choice Function). The conducted empirical study shows that HITO-DA obtained better results than MOEA/D in all cases, and obtained better results than HITO, in bigger systems
Ant colony system-based applications to electrical distribution system optimization
Chapter 16, February 201
Non-linear great deluge with learning mechanism for solving the course timetabling problem
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