| Inertia weight control strategies for particle swarm optimization KR Harrison, AP Engelbrecht, BM Ombuki-Berman Swarm Intelligence 10 (4), 267-305, 2016 | 49 | 2016 |
| Self-adaptive particle swarm optimization: a review and analysis of convergence KR Harrison, AP Engelbrecht, BM Ombuki-Berman Swarm Intelligence 12 (3), 187-226, 2018 | 45 | 2018 |
| Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm KR Harrison, AP Engelbrecht, BM Ombuki-Berman Swarm and evolutionary computation 41, 20-35, 2018 | 31 | 2018 |
| The bi-objective critical node detection problem M Ventresca, KR Harrison, BM Ombuki-Berman European Journal of Operational Research 265 (3), 895-908, 2018 | 22 | 2018 |
| The sad state of self-adaptive particle swarm optimizers KR Harrison, AP Engelbrecht, BM Ombuki-Berman 2016 IEEE Congress on Evolutionary Computation (CEC), 431-439, 2016 | 21 | 2016 |
| A meta-analysis of centrality measures for comparing and generating complex network models KR Harrison, M Ventresca, BM Ombuki-Berman Journal of computational science 17, 205-215, 2016 | 17 | 2016 |
| Knowledge transfer strategies for vector evaluated particle swarm optimization KR Harrison, B Ombuki-Berman, AP Engelbrecht International Conference on Evolutionary Multi-Criterion Optimization, 171-184, 2013 | 17 | 2013 |
| A parameter-free particle swarm optimization algorithm using performance classifiers KR Harrison, BM Ombuki-Berman, AP Engelbrecht Information Sciences 503, 381-400, 2019 | 16 | 2019 |
| An Experimental Evaluation of Multi-objective Evolutionary Algorithms for Detecting Critical Nodes in Complex Networks M Ventresca, KR Harrison, BM Ombuki-Berman Applications of Evolutionary Computation: 18th European Conference …, 2015 | 16 | 2015 |
| Optimal parameter regions for particle swarm optimization algorithms KR Harrison, BM Ombuki-Berman, AP Engelbrecht 2017 IEEE congress on evolutionary computation (CEC), 349-356, 2017 | 14 | 2017 |
| An adaptive particle swarm optimization algorithm based on optimal parameter regions KR Harrison, AP Engelbrecht, BM Ombuki-Berman 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2017 | 12 | 2017 |
| Dynamic multi-objective optimization using charged vector evaluated particle swarm optimization KR Harrison, BM Ombuki-Berman, AP Engelbrecht 2014 IEEE Congress on Evolutionary Computation (CEC), 1929-1936, 2014 | 8 | 2014 |
| A Scalability Study of Multi-Objective Particle Swarm Optimizers KR Harrison, AP Engelbrecht, BM Ombuki-Berman 2013 IEEE Congress on Evolutionary Computation (CEC), 189-197, 2013 | 7 | 2013 |
| A radius-free quantum particle swarm optimization technique for dynamic optimization problems KR Harrison, BM Ombuki-Berman, AP Engelbrecht 2016 IEEE Congress on Evolutionary Computation (CEC), 578-585, 2016 | 6 | 2016 |
| Incorporating expert knowledge in object-oriented genetic programming MR Medland, KR Harrison, B Ombuki-Berman Proceedings of the Companion Publication of the 2014 Annual Conference on …, 2014 | 6 | 2014 |
| Investigating fitness measures for the automatic construction of graph models KR Harrison, M Ventresca, BM Ombuki-Berman European Conference on the Applications of Evolutionary Computation, 189-200, 2015 | 5 | 2015 |
| Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks MR Medland, KR Harrison, BM Ombuki-Berman 2014 Sixth World Congress on Nature and Biologically Inspired Computing …, 2014 | 5 | 2014 |
| The parameter configuration landscape: A case study on particle swarm optimization KR Harrison, BM Ombuki-Berman, AP Engelbrecht 2019 IEEE Congress on Evolutionary Computation (CEC), 808-814, 2019 | 4 | 2019 |
| The Effect of Probability Distributions on the Performance of Quantum Particle Swarm Optimization for Solving Dynamic Optimization Problems KR Harrison, BM Ombuki-Berman, AP Engelbrecht Computational Intelligence, 2015 IEEE Symposium Series on, 242-250, 2015 | 4 | 2015 |
| Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming KR Harrison Brock University, 2014 | 4 | 2014 |
Andries EngelbrechtStellenbosch University, Department of Industrial Engineering, and Division of Computer ScienceVerified email at sun.ac.za
