Automatic Circle Detection on Images Based on an Evolutionary Algorithm That Reduces the Number of Function Evaluations

This paper presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on a newly developed evolutionary algorithm called the Adaptive Population with Reduced Evaluations (APRE). Our proposed algorithm reduces the number of function evaluations through the use of two mechanisms: (1) adapting dynamically the size of the population and (2) incorporating a fitness calculation strategy, which decides whether the calculation or estimation of the new generated individuals is feasible. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. Experimental results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique with regard to accuracy, speed, and robustness

Multidemand Multisource Order Quantity Allocation with Multiple Transportation Alternatives

This paper focuses on a multidemand multisource order quantity allocation problem with multiple transportation alternatives. To solve this problem, a bilevel multiobjective programming model under a mixed uncertain environment is proposed. Two levels of decision makers are considered in the model. On the upper level, the purchaser aims to allocate order quantity to multiple suppliers for each demand node with the consideration of three objectives: total purchase cost minimization, total delay risk minimization, and total defect risk minimization. On the lower level, each supplier attempts to optimize the transportation alternatives with total transportation and penalty costs minimization as the objective. In contrast to prior studies, considering the information asymmetry in the bilevel decision, random and fuzzy random variables are used to model uncertain parameters of the construction company and the suppliers. To solve the bilevel model, a solution method based on Kuhn-Tucker conditions, sectional genetic algorithm, and fuzzy random simulation is proposed. Finally, the applicability of the proposed model and algorithm is evaluated through a practical case from a large scale construction project. The results show that the proposed model and algorithm are efficient in dealing with practical order quantity allocation problems

Metaheurísticas de otimização aplicadas à sintonia dos ganhos de controlador PI multivariável

Orientador : Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 04/05/2016Inclui referências : f. 79-87Área de concentração: Sistemas eletrônicosResumo: Esta dissertação tem por objetivo avaliar abordagens de sintonia de controladores PI (Proporcional e Integral) multivariável e acoplado, utilizando metaheurísticas de otimização aplicada a soma ponderada dos sinais de erro do sistema. Os controladores PI e PID (Proporcional, Integral e Derivativo) são os controladores mais utilizados na indústria, pois possui um algoritmo simples e eficiente. Nesta dissertação, o algoritmo evolutivo denominado evolução diferencial (DE), é comparado a outros algoritmos derivados do DE clássico e também a outros algoritmos evolutivos baseados em população. Estes algoritmos são aplicados na otimização de controle PI em dois estudos de caso: um processo de uma caldeira de turbina (Boiler-Turbine) e um processo de controle de nível (Quadruple Tank). O processo de otimização lida com a soma ponderada dos sinais de erro dos sistemas tratando-os como um problema de otimização mono-objetivo. Nos dois estudos de caso o algoritmo que obteve o melhor desempenho entre todos os algoritmos foi o EPSDE (Ensemble of Mutation and Crossover Strategies and Parameters in DE), e o que apresentou o desempenho menos promissor entre todos os algoritmos testados foi o CMAES (do inglês, Covariance Matrix Adaptation Evolution Strategy). Entre os algoritmos baseados em população o que apresentou o pior desempenho nos dois estudos de caso foi o MVO (do inglês, Multi-Verse Optimization) e o que apresentou o melhor desempenho foi PSO (do inglês, Particle Swarm Optimization). Para o primeiro estudo de caso, o DE clássico teve um bom desempenho, o que não ocorreu no segundo estudo de caso. Os algoritmos variantes de DE apresentaram um bom desempenho para os dois estudos de caso quando comparados a outros algoritmos baseados em população aplicados nesta dissertação, concluindo assim, a eficácia dos algoritmos DE para os casos testados. Palavras-chave: Controle PI Multivariável, Metaheurísticas de Otimização, Algoritmo de Evolução Diferencial.Abstract: This thesis focuses on validate the approaches used for PI control (proportional and integral) multivariable and coupled using metaheuristics optimization applied the weighted sum of the system error signals. The PI and PID (proportional, integral and derivative) controllers are the controllers most commonly used in the industry because it has a simple and efficient algorithm. In this thesis the evolutionary algorithm named differential evolution (DE) is compared to other derived algorithms and also other evolutionary algorithms based on population. These algorithms are applied to the optimization of PI control in two case studies: a process of a boiler turbine (Boiler-Turbine) and level control process (Quadruple Tank). The optimization process deals with the weighted sum of the systems errors signals by treating them as a singleobjective optimization problem. In the two case studies the algorithm which obtained the best performance among all algorithms was the EPSDE (Ensemble of Mutation and Crossover Strategies and Parameters in DE) and presented the performance less promising among all algorithms tested was the CMAES (Covariance Matrix Adaptation Evolution Strategy). Among the algorithms based on population presented the worst performance in two case studies was the MVO (Multi-Verse Optimization) and presented the best performance was PSO (Particle Swarm Optimization). For the first case study, the classic DE had a good performance, which did not occur in the second case study. The algorithms DE variants performed well for the two case studies compared to other based population algorithms applied in this thesis, concluding thus the effectiveness of DE algorithms for the cases tested. Key-words: PI Control Multivariable, Optimization Metaheuristics, Differential Evolutionary Algorithm