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Nonconvex Economic Dispatch by Integrated Artificial IntelligenceCheng, Fu-Sheng 11 June 2001 (has links)
Abstract
This dissertation presents a new algorithm by integrating evolutionary programming (EP), tabu search (TS) and quadratic programming (QP), named the evolutionary-tabu quadratic programming (ETQ) method, to solve the nonconvex economic dispatch problem (NED). This problem involves the economic dispatch with valve-point effects (EDVP), economic dispatch with piecewise quadratic cost function (EDPQ), and economic dispatch with prohibited operating zones (EDPO). EDPV, EDPQ and EDPO are similar problems when ETQ was employed. The problem was solved in two phases, the cost-curve-selection subproblem, and the typical ED solving subproblem. The first phase was resolved by using a hybrid EP and TS, and the second phase by QP. In the solving process, EP with repairing strategy was used to generate feasible solutions, TS was used to prevent prematurity, and QP was used to enhance the performance. Numerical results show that the proposed method is more effective than other previously developed evolutionary computation algorithms.
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Otimização por Nuvem de Partículas e Busca Tabu para Problema da Diversidade MáximaBonotto, Edison Luiz 31 January 2017 (has links)
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Previous issue date: 2017-01-31 / The Maximu m Diversity Problem (MDP) is a problem of combinatorial optimization
area that aims to select a pre-set number of elements in a given set so that a sum of
the differences between the selected elements are greater as possible. MDP belongs
to the class of NP-Hard problems, that is, there is no known algorithm that solves
in polynomial time accurately. Because they have a complexity of exponential order,
require efficient heuristics to provide satisfactory results in acceptable time. However,
heuristics do not guarantee the optimality of the solution found. This paper proposes a
new hybrid approach for a resolution of the Maximum Diversity Problem and is based
on the Particle Swarm Optimization (PSO) and Tabu Search (TS) metaheuristics,
The algorithm is called PSO_TS. The use of PSO_TS achieves the best results for
known instances testing in the literature, thus demonstrating be competitive with the
best algorithms in terms of quality of the solutions. / O Problema da Diversidade Máxima (MDP) é um problema da área de Otimização
Combinatória que tem por objetivo selecionar um número pré-estabelecido de elementos
de um dado conjunto de maneira tal que a soma das diversidades entre os
elementos selecionados seja a maior possível. O MDP pertence a classe de problemas
NP-difícil, isto é, não existe algoritmo conhecido que o resolva de forma exata em
tempo polinomial. Por apresentarem uma complexidade de ordem exponencial, exigem
heurísticas eficientes que forneçam resultados satisfatórios em tempos aceitáveis.
Entretanto, as heurísticas não garantem otimalidade da solução encontrada. Este
trabalho propõe uma nova abordagem híbrida para a resolução do Problema da
Diversidade Máxima e está baseada nas meta-heurísticas de Otimização por Nuvem
de Partículas (PSO) e Busca Tabu(TS). O algoritmo foi denominado PSO_TS. Para
a validação do método, os resultados encontrados são comparados com os melhores
existentes na literatura.
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