To reduce the logistic cost and carbon emission improve customer satisfaction, this study proposes a multiobjective green time-dependent location routing problem (MOGTDLRP) model in which objectives are to minimize distribution total cost, delivery time, fuel consumption. This will be solved by several hyperheuristic algorithms include high-level heuristics low-level heuristics. There three acc...

J. Deon Garrett. Ph.D. The University of Memphis. February, 2008. Multiobjective Fitness Landscape Analysis and the Design of Effective Memetic Algorithms. Major Professor: Dipankar Dasgupta, Ph.D. For a wide variety of combinatorial optimization problems, no efficient algorithms exist to exactly solve the problem unless P=NP. For these problems, metaheuristics have come to dominate the landsca...

Optimization of the product portfolio has been recognized as a critical problem in industry, management, economy and so on. It aims at the selection of an optimal mix of the products to offer in the target market. As a probability function, reliability is an essential objective of the problem which linear models often fail to evaluate it. Here, we develop a multiobjective integer nonlinear cons...

In this paper, we are concerned with a fractional multiobjective optimization problem ( P ). Using support functions together generalized Guignard constraint qualification, give necessary optimality conditions in terms of convexificators and the Karush–Kuhn–Tucker multipliers. Several intermediate problems have been introduced to help us our investigation.

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one...

The environmental/economic dispatch (EED) problem is a large-scale highly constrained nonlinear multiobjective optimization problem. In recent years, this option has received much attention. Many approaches and methods have been reported to solve the multiobjective EED problem in the literature, such as genetic algorithms, artificial neural networks, particle swarm optimization, and differentia...

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes an multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving on...