The integrated circuit for 3D chip placement has attracted much attention in recent years, but its complex netlist structure poses significant challenges in optimizing 3D chip placement. However, the current algorithm...
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In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA-SQP, the mixedcoding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA-S...
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In this paper, mixed integer nonlinear programming (MINLP) is optimized by PSO_GA-SQP, the mixedcoding of a particle swarm optimization (PSO), and a hybrid genetic algorithm and sequential quadratic programming (GA-SQP). The population is separated into two groups: discrete and continuous variables. The discrete variables are optimized by the adapted PSO, while the continuous variables are optimized by the GA-SQP using the discrete variable information from the adapted PSO. Therefore, the population can be set to a smaller size than usual to obtain a global solution. The proposed PSO_GA-SQP algorithm is verified using various MINLP problems including the designing of retrofit heat exchanger networks. The fitness values of the tested problems are able to reach the global optimum.
In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineerin...
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In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.
In this paper, an improved hybrid differential evolution algorithm (IHDE) is proposed for nonlinear and mixed-integer nonlinear programming models (NLPs and MINLPs) in engineering optimization fields. In order to impr...
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In this paper, an improved hybrid differential evolution algorithm (IHDE) is proposed for nonlinear and mixed-integer nonlinear programming models (NLPs and MINLPs) in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE takes full advantage of differential information and global statistical information extracted respectively by differential evolution algorithm (DE) and the annealing mechanism-embedded estimation of distribution algorithm (EDA), and adopts the feasibility rules to handle constraints. Simulation and comparison based on three bench-marks and a practical scheduling of crude oil blending problem demonstrate the efficiency, accuracy and robustness of IHDE. Moreover, the key parameters of IHDE are also analyzed.
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