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Shengxiang Yang

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2 papers

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AIJ Journal 2015 Journal Article

Bi-goal evolution for many-objective optimization problems

Miqing Li
Shengxiang Yang
Xiaohui Liu

This paper presents a meta-objective optimization approach, called Bi-Goal Evolution (BiGE), to deal with multi-objective optimization problems with many objectives. In multi-objective optimization, it is generally observed that 1) the conflict between the proximity and diversity requirements is aggravated with the increase of the number of objectives and 2) the Pareto dominance loses its effectiveness for a high-dimensional space but works well on a low-dimensional space. Inspired by these two observations, BiGE converts a given multi-objective optimization problem into a bi-goal (objective) optimization problem regarding proximity and diversity, and then handles it using the Pareto dominance relation in this bi-goal domain. Implemented with estimation methods of individuals' performance and the classic Pareto nondominated sorting procedure, BiGE divides individuals into different nondominated layers and attempts to put well-converged and well-distributed individuals into the first few layers. From a series of extensive experiments on four groups of well-defined continuous and combinatorial optimization problems with 5, 10 and 15 objectives, BiGE has been found to be very competitive against five state-of-the-art algorithms in balancing proximity and diversity. The proposed approach is the first step towards a new way of addressing many-objective problems as well as indicating several important issues for future development of this type of algorithms.

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EAAI Journal 2010 Journal Article

Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks

Hui Cheng
Shengxiang Yang

In this paper, the problem of dynamic quality-of-service (QoS) multicast routing in mobile ad hoc networks is investigated. Lots of interesting works have been done on multicast since it is proved to be a NP-hard problem. However, most of them consider the static network scenarios only and the multicast tree cannot adapt to the topological changes. With the advancement in communication technologies, more and more wireless mobile networks appear, e. g. , mobile ad hoc networks (MANETs). In a MANET, the network topology keeps changing due to its inherent characteristics such as the node mobility and energy conservation. Therefore, an effective multicast algorithm should track the topological changes and adapt the best multicast tree to the changes accordingly. In this paper, we propose to use genetic algorithms with immigrants schemes to solve the dynamic QoS multicast problem in MANETs. MANETs are considered as target systems because they represent a new generation of wireless networks. In the construction of the dynamic network environments, two models are proposed and investigated. One is named as the general dynamics model in which the topologies are changed due to that the nodes are scheduled to sleep or wake up. The other is named as the worst dynamics model, in which the topologies are altered because some links on the current best multicast tree are removed. Extensive experiments are conducted based on both of the dynamic network models. The experimental results show that these immigrants based genetic algorithms can quickly adapt to the environmental changes (i. e. , the network topology changes) and produce high quality solutions following each change.

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