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...

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-...

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valvepoint loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED prob...

This paper proposes a multi-objective memetic algorithm based on NSGA-II and Simulated Annealing (SA), NSGA-II-SA, for calibration of microscopic vehicular traffic flow simulation models. The NSGA-II algorithm performs a scan in the search space and obtains the Pareto front which is optimized locally with SA. The best solution of the obtained front is selected. Two CORSIM models were calibrated...

The nondominated sorting genetic algorithm with elitism (NSGA-II) is widely used due to its good performance on solving multiobjective optimization problems. In each iteration of NSGA-II, truncation selection is performed based on the rank and crowding distance of each solution. There are, however, drawbacks in this process. These drawbacks to some extent cause overlapping solutions in the popu...

This paper presented a parallel hybrid electric vehicle (HEV) equipped with a hybrid energy storage system. To handle complex energy flow in the powertrain system of this HEV, a fuzzy-based energy management strategy was established. A chaotic multi-objective genetic algorithm, which optimizes the parameters of fuzzy membership functions, was also proposed to improve fuel economy and HC, CO, an...

Based on the analysis on the basic principles and characteristics of the existing multiobjective genetic algorithm (MOGA), an improved multi-objective GA with elites maintain is put forward based on non-dominated sorting genetic algorithm (NSGA). NSGA-II algorithm theory and parallel hybrid evolutionary theory is described in detail. The design principle, process and detailed implementations of...

A multiobjective optimization problem which focuses on parallel machines scheduling is considered. This problem consists of scheduling n independent jobs onm identical parallel machines with release dates, due dates, and sequence-dependent setup times. The preemption of jobs is forbidden. The aim is to minimize two different objectives: makespan and total tardiness. The contribution of this pap...

Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality co...

Abstract. This paper examines the effect of crossover operations on the performance of EMO algorithms through computational experiments on knapsack problems and flowshop scheduling problems using the NSGA-II algorithm. We focus on the relation between the performance of the NSGA-II algorithm and the similarity of recombined parent solutions. First we show the necessity of crossover operations t...