A multiobjective state transition algorithm for single machine scheduling
- Authors: Zhou, Xiaojun , Hanoun, Samer , Gao, David , Nahavandi, Saeid
- Date: 2015
- Type: Text , Conference paper
- Relation: 3rd World Congress on Global Optimization in Engineering and Science, WCGO 2013; Anhui, China; 8th-12th July 2013 Vol. 95, p. 79-88
- Full Text: false
- Reviewed:
- Description: In this paper, a discrete state transition algorithm is introduced to solve a multiobjective single machine job shop scheduling problem. In the proposed approach, a non-dominated sort technique is used to select the best from a candidate state set, and a Pareto archived strategy is adopted to keep all the non-dominated solutions. Compared with the enumeration and other heuristics, experimental results have demonstrated the effectiveness of the multiobjective state transition algorithm. © Springer International Publishing Switzerland 2015.
An efficient classification using support vector machines
- Authors: Ruan, Ning , Chen, Yi , Gao, David
- Date: 2013
- Type: Text , Conference paper
- Relation: Proceedings of 2013 Science and Information Conference, SAI 2013 p. 585-589
- Full Text: false
- Reviewed:
- Description: Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization problems are equivalent to a unified concave maximization problem over a convex set and hence can be solved efficiently by existing optimization techniques. © 2013 The Science and Information Organization.