In many real-world applications, various optimization problems with conflicting objectives are very common. In this paper we employ Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), a newly developed method, beside Tabu Search (TS) accompaniment to achieve a new manner for solving multi-objective optimization problems (MOPs) with two or three conflicting objectives. This i...

The transit network design problem (TNDP) aims to find a set of routes and corresponding frequencies for an urban public transportation system. We model the TNDP as a multi-objective combinatorial optimization problem whose resolution involves finding a Pareto front that represents different trade-off levels between opposite objectives, the travel and waiting times and the required fleet of bus...

In multi-objective optimization the hypervolume indicator is a measure for the size of the space within a reference set that is dominated by a set of μ points. It is a common performance indicator for judging the quality of Pareto front approximations. As it does not require a-priori knowledge of the Pareto front it can also be used in a straightforward manner for guiding the search for finite ...

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized ...

This paper presents the integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a co-operative co-evolutionary genetic algorithm (CCGA). The resulting algorithm is referred to as a multi-objective co-operative co-evolutionary genetic algorithm or MOCCGA. The integration between the two algorithms is carried out in order to improve the performance of th...

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized ...

In the first part (Part I) of this study, a novel fluidized bed reactor was modeled mathematically for methanol synthesis in the presence of in-situ water adsorbent named Sorption Enhanced Fluidized-bed Reactor (SE-FMR) is modeled, mathematically. Here, the non-dominated sorting genetic algorithm-II (NSGA-II) is applied for multi-objective optimization of this configuration. Inlet temperature o...

Distribution centers (DCs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.This paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. An evolutionary algorithm named non-dominated sorting ant colony optimization (NSACO) is used as the optimi...

Hybrid algorithms that combine genetic algorithms with the Nelder-Mead simplex algorithm have been effective in solving certain optimization problems. In this article, we apply a similar technique to estimate the parameters of a gene regulatory network for flowering time control in rice. The algorithm minimizes the difference between the model behavior and real world data. Because of the nature...