Research on Multi-Cooperative Combine-Integrated Scheduling Based on Improved NSGA-II Algorithm

An Improved Algorithm Based on NSGA-II for Cloud PDTs Scheduling

Partly dependent tasks (PDTs) scheduling with multi-objective optimization in cloud computing is an NP-hard problem. Taking the quality of service (QoS) requirements of users that use cloud computing into account, we set the cost and time requirements of handling the PDTs as the multiple objectives and present an improved algorithm based on the non-dominated sorting genetic algorithm-II (NSGA-I...

Improved NSGA-II Based on a Novel Ranking Scheme

Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multiobjective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm suffers from a high order of complexity, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce ...

A Multi-objective Cross-training Plan Based on NSGA-II

The previous research on cross-training mainly focused on productive efficiency. However, enhancing labor’s satisfaction of tasks is as much important as improving production performance. This paper addresses a new cross-training policy for an assembly cell from the point of view of humanization. A multi-objective 0-1 integer programming model is presented to implement the crosstraining policy ...

An Improved NSGA-II Algorithm for Multi-objective Traveling Salesman Problem

Multi-objective traveling salesman problem (MOTSP) is an extended instance of traveling salesman problem (TSP), which is a well-known NP hard problem. In this paper, an improved NSGA-II algorithm (denoted by INSGA-II-MOTSP) is proposed to solve the MOTSP. Specifically, a layer strategy according to need is proposed to avoid generating unnecessary non-dominated fronts. The arena’s principle is a...

A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II