A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization
This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multiobjective optimization problems and to track the Pareto front in a dynamic environment.
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
- Chong Zhang, Pin Lim, A. K. Qin, K. Tan
- Computer ScienceIEEE Transactions on Neural Networks and Learning…
- 1 October 2017
A multiobjective deep belief networks ensemble (MODBNE) method that employs a multiobjectives evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives is proposed.
Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
- Yuan Yuan, Y. Ong, H. Ishibuchi
- Computer ScienceArXiv
- 8 June 2017
Nine test problems for multi-task multi-Objective optimization (MTMOO), each of which consists of two multiobjective optimization tasks that need to be solved simultaneously, are suggested.
Heuristic methods for vehicle routing problem with time windows
- K. Tan, L. Lee, Kenny Q. Zhu, Ke Ou
- Business, Computer ScienceArtificial Intelligence in Engineering
- 1 July 2001
Multiobjective Multifactorial Optimization in Evolutionary Multitasking
- Abhishek Gupta, Y. Ong, Liang Feng, K. Tan
- Computer ScienceIEEE Transactions on Cybernetics
- 1 July 2017
This paper presents a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization, which leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics.
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
Three noise-handling features are proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties.
Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons
- K. Tan, Tong-heng Lee, E. F. Khor
- Computer ScienceProceedings of the Congress on Evolutionary…
- 27 May 2001
A survey on variousevolutionary methods for MO optimization by considering the usual performancemeasures in MO optimization and a few metrics to examinethe strength and weakness of each evolutionary approach both quantitatively and qualitatively.
A Multi-Facet Survey on Memetic Computation
- Xianshun Chen, Y. Ong, M. Lim, K. Tan
- Computer ScienceIEEE Transactions on Evolutionary Computation
- 1 October 2011
A comprehensive multi-facet survey of recent research in memetic computation is presented and includes simple hybrids, adaptive hybrids and memetic automaton.
A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment
- Wee Tat Koo, C. Goh, K. Tan
- Computer ScienceMemetic Computing
- 1 June 2010
This work proposes the novel approach of tracking and predicting the changes in the location of the Pareto Set in order to minimize the effects of a landscape change and incorporated into a variant of the multi-objective evolutionary gradient search (MO-EGS), and two other MOEAs for dynamic optimization.
Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns
- Qiang Yu, Huajin Tang, K. Tan, Haizhou Li
- Computer SciencePLoS ONE
- 5 November 2013
Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion.