Browsing by Author "Nguyen, T. T."
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Item Open Access An Adaptive Multi-Population Framework for Locating and Tracking Multiple Optima(IEEE Press, 2015-11-30) Li, Changhe; Nguyen, T. T.; Ming, Yang; Mavrovouniotis, Michalis; Yang, ShengxiangMulti-population methods are effective to solve dynamic optimization problems. However, to efficiently track multiple optima, algorithm designers need to address a key issue: how to adapt the number of populations. In this paper, an adaptive multi-population framework is proposed to address this issue. A database is designed to collect heuristic information of algorithm behavior changes. The number of populations is adjusted according to statistical information related to the current evolving status in the database and a heuristic value. Several other techniques are also introduced, including a heuristic clustering method, a population exclusion scheme, a population hibernation scheme, two movement schemes, and a peak hiding method. The particle swarm optimization and differential evolution algorithms are implemented into the framework, respectively. A set of multi-population based algorithms are chosen to compare with the proposed algorithms on the moving peaks benchmark using four different performance measures. The effect of the components of the framework is also investigated based on a set of multi-modal problems in static environments. Experimental results show that the proposed algorithms outperform the other algorithms in most scenarios.Item Open Access Benchmark generator for CEC 2009 competition on dynamic optimization(University of Leicester, U.K., 2008-10) Li, Changhe; Yang, Shengxiang; Nguyen, T. T.; Ling Yu, E.; Yao, Xin; Jin, Yaochu; Beyer, H. -G.; Suganthan, P. N.Item Metadata only Benchmark generator for CEC 2009 competition on dynamic optimization. Technical Report 2008.(Department of Computer Science, University of Leicester., 2008) Li, Changhe; Yang, Shengxiang; Nguyen, T. T.; Yu, E. L.; Yao, Xin; Jin, Yaochu; Beyer, H. -G.; Suganthan, P. N.Item Metadata only Evolutionary dynamic optimization: methodologies.(Springer-Verlag., 2013) Nguyen, T. T.; Yang, ShengxiangItem Metadata only Evolutionary dynamic optimization: test and evaluation environments.(Springer-Verlag., 2013) Yang, Shengxiang; Nguyen, T. T.; Li, ChangheItem Metadata only Metaheuristics for dynamic combinatorial optimization problems.(The Institute of Mathematics and its Applications., 2012) Yang, Shengxiang; Jiang, Y.; Nguyen, T. T.Many real-world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheuristics are a good choice of tools to tackle DCOPs because many metaheuristics are inspired by natural or biological evolution processes, which are always subject to changing environments. In recent years, DCOPs have attracted a growing interest from the metaheuristics community. This paper is a tutorial on metaheuristics for DCOPs. We cover the definition of DCOPs, typical benchmark problems and their characteristics, methodologies and performance measures, real-world case study and key challenges in the area. Some future research directions are also pointed out in this paper.Item Metadata only Multi-population methods in unconstrained continuous dynamic environments: the challenges(Elsevier, 2015-03) Li, Changhe; Nguyen, T. T.; Yang, Ming; Yang, Shengxiang; Zeng, SanyouThe multi-population method has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing peaks simultaneously. However, to make this approach efficient, several crucial challenging issues need to be addressed, e.g., how to determine the moment to react to changes, how to adapt the number of populations to changing environments, and how to determine the search area of each population. In addition, several other issues, e.g., communication between populations, overlapping search, the way to create populations, detection of changes, and local search operators, should be also addressed. The lack of attention on these challenging issues within multi-population methods hinders the development of multi-population based algorithms in dynamic environments. In this paper, these challenging issues are comprehensively analyzed by a set of experimental studies from the algorithm design point of view. Experimental studies based on a set of popular algorithms show that the performance of algorithms is significantly affected by these challenging issues on the moving peaks benchmark.Item Metadata only A self-learning particle swarm optimizer for global optimization problems(2012-06) Li, Changhe; Yang, Shengxiang; Nguyen, T. T.Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.