Bibliography¶

Pre-2000¶

[1] A. Engelbrecht, “Sensitivity Analysis for Decision Boundaries,” Neural Processing Letters, vol. 10, no. 3, pp. 253–266, 1999, doi: https://doi.org/10.1023/A:1018748928965.

[2] A. Engelbrecht and A. Ismail, “Training Product Unit Neural Networks,” Stability and Control: Theory and Applications, vol. 2, no. ½, pp. 59–74, 1999 [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.3872

2000 to 2004¶

[3] A. Engelbrecht, “Using the Taylor Expansion of Multilayer Feedforward Neural Networks,” South African Computer Journal, no. 26, pp. 181–189, 2000 [Online]. Available: https://www.semanticscholar.org/paper/Using-the-Taylor-expansion-of-multilayer-neural-Engelbrecht/0ed87605fbfe331031235f0f3b7298bf8b50c246

[4] F. van den Bergh and A. Engelbrecht, “Cooperative Learning in Neural Networks using Particle Swarm Optimizers,” South African Computer Journal, no. 26, pp. 84–90, 2000 [Online]. Available: https://www.semanticscholar.org/paper/Cooperative-learning-in-neural-networks-using-swarm-Bergh-Engelbrecht/aec1538916f6a6ec3a30ea6b8e84d33a9bb741a2

[5] A. Engelbrecht, “Sensitivity Analysis for Selective Learning by Feedforward Neural Networks,” Fundamenta Informaticae, vol. 46, no. 3, pp. 219–252, 2001 [Online]. Available: https://content.iospress.com/articles/fundamenta-informaticae/fi46-3-03

[6] A. Engelbrecht, “A New Pruning Heuristic Based on Variance Analysis of Sensitivity Information,” IEEE Transactions on Neural Networks, vol. 12, no. 6, pp. 1386–1399, 2001, doi: https://doi.org/10.1109/72.963775.

[7] A. Engelbrecht and R. Brits, “Supervised Training Using an Unsupervised Approach to Active Learning,” Neural Processing Letters, vol. 15, pp. 247–260, 2002, doi: https://doi.org/10.1023/A:1015733517815.

[8] L. Messerschmidt and A. Engelbrecht, “Learning to Play Games using a PSO-Based Competitive Learning Approach,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 280–288, 2004, doi: https://doi.org/10.1109/TEVC.2004.826070.

[9] F. van den Bergh and A. Engelbrecht, “A Cooperative Approach to Particle Swarm Optimisation,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 225–239, 2004, doi: https://doi.org/10.1109/TEVC.2004.826069.

[10] N. Franken and A. Engelbrecht, “Evolving intelligent game-playing agents,” South African Computer Journal, no. 32, pp. 44–52, 2004 [Online]. Available: https://journals.co.za/content/comp/2004/32/EJC27960

[11] D. Rodic and A. Engelbrecht, “Social Networks as a Task Allocation Tool for Multi-Robot Teams,” South African Computer Journal, no. 33, pp. 52–66, 2004 [Online]. Available: https://journals.co.za/content/comp/2004/33/EJC27971

2005 to 2009¶

[12] A. Salman, M. Omran, and A. Engelbrecht, “SIGT: Synthetic Image Generation Tool for Clustering Algorithms,” ICGST International Journal on Graphics, Vision and Image Processing, vol. 2, pp. 33–44, 2005 [Online]. Available: https://www.academia.edu/21254668/SIGT_Synthetic_image_generation_tool_for_clustering_algorithms_

[13] M. Omran, A. Engelbrecht, and A. Salman, “Particle Swarm Optimization Method for Image Clustering,” International Journal on Pattern Recognition and Artificial Intelligence, vol. 19, no. 3, pp. 297–322, 2005, doi: https://doi.org/10.1142/S0218001405004083.

[14] M. Omran, A. Engelbrecht, and A. Salman, “A Color Image Quantization Algorithm Based on Particle Swarm Optimization,” Informatica Journal, vol. 29, no. 3, pp. 263–271, 2005 [Online]. Available: http://www.informatica.si/index.php/informatica/article/view/40

[15] M. Omran, A. Engelbrecht, and A. Salman, “A PSO-Based End-Member Selection Method for Spectral Unmixing of Multispectral Satellite Images,” International Journal of Computational Intelligence, vol. 2, no. 2, pp. 124–132, 2005 [Online]. Available: https://waset.org/Publication/a-pso-based-end-member-selection-method-for-spectral-unmixing-of-multispectral-satellite-images/7090

[16] N. Franken and A. Engelbrecht, “Particle Swarm Optimization Approaches to Coevolve Strategies for the Iterated Prisoner’s Dilemma,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 6, pp. 562–579, 2005, doi: https://doi.org/10.1109/TEVC.2005.856202.

[17] W. Duminy and A. Engelbrecht, “Composing linear evaluation functions from observable features,” South African Computer Journal, vol. 35, pp. 48–58, 2005 [Online]. Available: https://journals.co.za/content/comp/2005/35/EJC27997

[18] M. Omran, A. Salman, and A. Engelbrecht, “Dynamic Clustering using Particle Swarm Optimization with Application in Image Segementation,” Pattern Analysis and Applications Journal, vol. 8, no. 4, pp. 332–344, 2006, doi: https://doi.org/10.1007/s10044-005-0015-5.

[19] F. van den Bergh and A. Engelbrecht, “A Study of Particle Swarm Optimization Particle Trajectories,” Information Sciences, vol. 176, no. 8, pp. 937–971, 2006, doi: https://doi.org/10.1016/j.ins.2005.02.003.

[20] A. Graaff and A. Engelbrecht, “Optimised Coverage of Non-self with Evolved Lymphocytes in Artificial Immune Systems,” International Journal of Computational Intelligence Research, vol. 2, no. 2, pp. 127–150, 2006 [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.324.4581

[21] P. Lutu and A. Engelbrecht, “A Comparative Study of Sample Selection Methods for Data Mining,” South African Computer Journal, no. 36, pp. 69–85, 2006 [Online]. Available: https://journals.co.za/content/comp/2006/36/EJC28009

[22] U. Paquet and A. Engelbrecht, “Particle Swarms for Equality-Constrained Optimization,” Fundamenta Informaticae, vol. 76, nos. 1-2, pp. 147–170, 2007 [Online]. Available: https://dl.acm.org/citation.cfm?id=1232705

[23] G. Potgieter and A. Engelbrecht, “Genetic Algorithms for the Structural Optimisation of Learned Polynomial Expressions,” Applied Mathematics and Computation, vol. 186, pp. 1441–1466, 2007, doi: https://doi.org/10.1016/j.amc.2006.07.164.

[24] S. Khan and A. Engelbrecht, “A New Fuzzy Operator and Its Application to Topology Design of Distributed Local Area Networks,” Information Sciences, vol. 177, no. 13, pp. 2692–2711, 2007, doi: https://doi.org/10.1016/j.ins.2007.01.031.

[25] M. Omran, A. Engelbrecht, and S. Ayed, “Empirical Analysis of Self-Adaptive Differential Evolution,” European Journal of Operational Research, vol. 183, no. 2, pp. 785–804, 2007, doi: https://doi.org/10.1016/j.ejor.2006.10.020.

[26] M. Omran, A. Engelbrecht, and S. Ayed, “An Overview of Clustering Methods,” Intelligent Data Analysis, vol. 11, no. 6, pp. 583–605, 2007 [Online]. Available: https://dl.acm.org/citation.cfm?id=1368020

[27] R. Brits, A. Engelbrecht, and F. van den Bergh, “Locating Multiple Optima using Particle Swarm Optimization,” Applied Mathematics and Computation, vol. 189, no. 2, pp. 1859–1883, 2007, doi: https://doi.org/10.1016/j.amc.2006.12.066.

[28] M. Omran, A. Engelbrecht, M. Zraibi, and E. Omran, “Empirical Analysis of Using Neighborhood Topologies with Differential Evolution,” Advances in Computer Science and Engineering Journal, vol. 1, no. 3, pp. 189–222, 2008.

[29] D. Rodic and A. Engelbrecht, “Social Networks in Simulated Multi-Robot Environment,” International Journal of Intelligent Computing and Cybernetics, vol. 1, no. 1, pp. 110–127, 2008, doi: https://doi.org/10.1108/17563780810857158.

[30] G. Potgieter and A. Engelbrecht, “Evolving Model Trees for Mining Data Sets with Continuous-Valued Classes,” Expert Systems with Applications, vol. 35, pp. 1513–1532, 2008, doi: https://doi.org/10.1016/j.eswa.2007.08.060.

[31] M. Dorigo, M. M. de Oca, and A. Engelbrecht, “Particle Swarm Optimization,” Scholarpedia, vol. 3, no. 11, p. 1486, 2008 [Online]. Available: http://www.scholarpedia.org/article/Particle_swarm_optimization

[32] N. Khalid, Z. Ibrahim, T. Kurniawan, M. Khalid, and A. Engelbrecht, “DNA Sequence Optimization Based on Continuous Particle Swarm Optimization for Reliable DNA Computing and DNA Nanotechnology,” Journal of Computer Science, vol. 4, no. 11, pp. 942–950, 2008, doi: https://doi.org/10.3844/jcssp.2008.942.950.

[33] S. Khan and A. Engelbrecht, “Fuzzy Hybrid Simulated Annealing Algorithms for Topology Design of Switched Local Area Networks,” Soft Computing, vol. 13, no. 1, pp. 45–61, 2009, doi: https://doi.org/10.1007/s00500-008-0292-1.

[34] N. Khalid, Z. Ibrahim, T. Kurniawan, M. Khalid, N. Sarmin, and A. Engelbrecht, “Function Minimization in DNA Sequence Design based on Continuous Particle Swarm Optimization,” Innovative Computing, Information and Control Express Letters, vol. 3, no. 1, pp. 27–32, 2009 [Online]. Available: https://www.semanticscholar.org/paper/Function-minimization-in-DNA-sequence-design-based-Khalid-Ibrahim/ec79bc31a1099a360a0527204011c9e94925f714

[35] M. Omran, A. Engelbrecht, and A. Salman, “Bare Bones Differential Evolution,” European Journal of Operational Research, vol. 196, no. 1, pp. 128–139, 2009, doi: https://doi.org/10.1016/j.ejor.2008.02.035.

2010 to 2014¶

[36] I. Schoeman and A. Engelbrecht, “A Novel Particle Swarm Niching Technique based on Extensive Vector Operations,” Natural Computing, vol. 9, no. 3, pp. 683–701, 2010, doi: https://doi.org/10.1007/s11047-009-9170-8.

[37] J. Grobler, A. Engelbrecht, S. Kok, and S. Yadavalli, “Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time,” Annals of Operations Research, vol. 180, no. 1, pp. 165–196, 2010, doi: https://doi.org/10.1007/s10479-008-0501-4.

[38] P. Lutu and A. Engelbrecht, “Decision Rule-based Method for Feature Selection in Predictive Mining,” Expert Systems with Applications, vol. 37, no. 1, pp. 602–609, 2010, doi: https://doi.org/10.1016/j.eswa.2009.06.031.

[39] M. Mtshali and A. Engelbrecht, “Robotic Architectures: A Review,” Defence Science Journal, vol. 60, no. 1, pp. 15–22, 2010 [Online]. Available: https://researchspace.csir.co.za/dspace/handle/10204/4643

[40] F. van den Bergh and A. Engelbrecht, “A Convergence Proof for the Particle Swarm Optimizer,” Fundamenta Informaticae, vol. 105, no. 4, pp. 341–374, 2010, doi: https://dl.acm.org/citation.cfm?id=2010421.

[41] W. Matthysen and A. Engelbrecht, “A Polar Coordinate Particle Swarm Optimiser,” Applied Soft Computing, vol. 11, no. 1, pp. 1322–1339, 2011, doi: https://doi.org/10.1016/j.asoc.2010.04.005.

[42] A. Graaff and A. Engelbrecht, “Clustering Data in an Uncertain Environment using an Artificial Immune System,” Pattern Recognition Letters, vol. 32, no. 2, pp. 342–351, 2011, doi: https://doi.org/10.1016/j.patrec.2010.09.013.

[43] A. Graaff and A. Engelbrecht, “Using Sequential Deviation to Dynamically Determine the Number of Clusters Found by a Local Network Neighbourhood Artificial Immune System,” Applied Soft Computing, vol. 11, no. 2, pp. 2698–2713, 2011, doi: https://doi.org/10.1016/j.asoc.2010.10.017.

[44] A. Louis and A. Engelbrecht, “Unsupervised Discovery of Relations for Analysis of Textual Data in Digital Forensics,” Digitial Investigation: The International Journal of Digital Forensics & Incident Response, vol. 7, nos. 3-4, pp. 154–171, 2011, doi: https://doi.org/10.1016/j.diin.2010.08.004.

[45] P. Lutu and A. Engelbrecht, “Using OVA Modeling to Improve Classification Performance for Large Datasets,” Expert Systems With Applications, vol. 39, no. 4, pp. 4358–4376, 2012, doi: https://doi.org/10.1016/j.eswa.2011.09.156.

[46] A. Graaff and A. Engelbrecht, “Clustering Data in Stationary Environments with a Local Network Neighborhood Artificial Immune System,” International Journal of Machine Learning and Cybernetics, vol. 3, no. 1, pp. 1–26, 2012, doi: https://doi.org/10.1007/s13042-011-0041-0.

[47] S. Khan and A. Engelbrecht, “A Fuzzy Particle Swarm Optimization Algorithm for Computer Communication Network Topology Design,” Applied Intelligence Journal, vol. 36, no. 1, pp. 161–177, 2012, doi: https://doi.org/10.1007/s10489-010-0251-2.

[48] M. du Plessis and A. Engelbrecht, “Using Competitive Population Evaluation in a Differential Evolution algorithm for Dynamic Environments,” European Journal of Operational Research, vol. 218, no. 1, pp. 7–20, 2012, doi: https://doi.org/10.1016/j.ejor.2011.08.031.

[49] A. Rakitianskaia and A. Engelbrecht, “Training Feedforward Neural Networks with Dynamic Particle Swarm Optimisation,” Swarm Intelligence, vol. 6, no. 3, pp. 233–270, 2012, doi: https://doi.org/10.1007/s11721-012-0071-6.

[50] J. Langeveld and A. Engelbrecht, “Set-Based Particle Swarm Optimization applied to the Multidimensional Knapsack Problem,” Swarm Intelligence, vol. 6, no. 4, pp. 297–342, 2012, doi: https://doi.org/10.1007/s11721-012-0071-6.

[51] M. du Plessis and A. Engelbrecht, “Differential Evolution for Dynamic Environments with Unknown Numbers of Optima,” Global Optimization, vol. 55, no. 1, pp. 73–99, 2013, doi: https://doi.org/10.1007/s10898-012-9864-9.

[52] P. Antoniou, A. Pitsillides, T. Blackwell, A. Engelbrecht, and L. Michael, “Congestion Control in Wireless Sensor Networks based on Bird Flocking Behavior,” Computer Networks, vol. 57, no. 5, pp. 1167–1191, 2013, doi: https://doi.org/10.1016/j.comnet.2012.12.008.

[53] P. Lutu and A. Engelbrecht, “Base Model Combination Algorithm for Resolving Tied Predictions for K-Nearest Neighbour OVA Ensemble Models,” INFORMS Journal on Computing, vol. 25, no. 3, 2013, doi: https://doi.org/10.1287/ijoc.1120.0518.

[54] P. Lutu and A. Engelbrecht, “Positive-Versus-Negative Classification for Model Aggregation in Predictive Data Mining,” NFORMS Journal on Computing, vol. 25, no. 4, 2013, doi: https://doi.org/10.1287/ijoc.1120.0540.

[55] M. Poggiolini and A. Engelbrecht, “The Application of the Feature-Detection Rule to the Negative Selection Algorithm,” Expert Systems with Applications, vol. 40, no. 8, pp. 3001–3014, 2013, doi: https://doi.org/10.1016/j.eswa.2012.12.016.

[56] M. Helbig and A. Engelbrecht, “Performance Measures for Dynamic Multi-objective Optimisation Algorithms,” Information Sciences, vol. 250, pp. 61–81, 2013, doi: https://doi.org/10.1016/j.ins.2013.06.051.

[57] K. Malan and A. Engelbrecht, “A Survey of Techniques for Characterising Fitness Landscapes and Some Possible Ways Forward,” Information Sciences, vol. 241, pp. 148–163, 2013, doi: https://doi.org/10.1016/j.ins.2013.04.015.

[58] M. Helbig and A. Engelbrecht, “Population-based Metaheuristics for Continuous Boundary-Constrained Dynamic Multi-Objective Optimisation Problems,” Swarm Intelligence and Evolutionary Computation, vol. 14, pp. 31–47, 2014, doi: https://doi.org/10.1016/j.swevo.2013.08.004.

[59] M. Helbig and A. Engelbrecht, “Benchmarks for Dynamic Multi-objective Optimisation Algorithms,” ACM Computing Surveys, vol. 46, no. 3, 2014, doi: https://doi.org/10.1145/2517649.

[60] C. Cleghorn and A. Engelbrecht, “A Generalized Theoretical Deterministic Particle Swarm Model,” Swarm Intelligence, vol. 8, no. 1, pp. 35–59, 2014, doi: https://doi.org/10.1007/s11721-013-0090-y.

[61] K. Malan and A. Engelbrecht, “Characterising the searchability of continuous optimisation problems for PSO,” Swarm Intelligence, vol. 8, no. 4, pp. 275–302, 2014, doi: https://doi.org/10.1007/s11721-014-0099-x.

[62] A. Mohiuddin, S. Khan, and A. Engelbrecht, “Simulated Evolution and Simulated Annealing Algorithms for Solving Multi-objective Open Shortest Path First Weight Setting Problem,” Applied Intelligence, vol. 41, pp. 348–365, 2014, doi: https://doi.org/10.1007/s10489-014-0523-3.

2015 to 2019¶

[63] J. Grobler, A. Engelbrecht, G. Kendall, and V. Yadavalli, “Heuristic Space Diversity for Improved Meta-Hyperheuristics Performance,” Information Sciences, vol. 300, pp. 49–62, 2015, doi: https://doi.org/10.1016/j.ins.2014.11.012.

[64] A. Dymond, A. Engelbrecht, S. Kok, and P. Heyns, “Tuning Optimization Algorithms under Multiple Objective Function Evaluation Budgets,” IEEE Transactions on Evolutionary Computation, vol. 19, no. 3, pp. 341–358, 2015, doi: https://doi.org/10.1109/TEVC.2014.2322883.

[65] C. Cleghorn and A. Engelbrecht, “Particle Swarm Variants: Standardized Convergence Analysis,” Swarm Intelligence, vol. 9, nos. 2-3, pp. 177–203, 2015, doi: https://doi.org/10.1007/s11721-015-0109-7.

[66] B. Leonard, A. Engelbrecht, and C. Cleghorn, “Critical Considerations on Angle Modulated Particle Swarm Optimisers,” Swarm Intelligence, vol. 9, no. 4, pp. 291–314, 2015, doi: https://doi.org/0.1007/s11721-015-0114-x.

[67] C. Scheepers and A. Engelbrecht, “Training Multi-Agent Teams from Zero Knowledge with the Competitive Coevolutionary team-based Particle Swarm Optimiser,” Soft Computing, vol. 20, no. 2, pp. 607–620, 2016, doi: https://doi.org/10.1007/s00500-014-1525-0.

[68] “Particle Swarm Optimization with Crossover: A Review and Empirical Analysis,” Artificial Intelligence Review, vol. 45, no. 2, pp. 131–165, 2016, doi: https://doi.org/10.1007/s10462-015-9445-7.

[69] M. Mohiuddin, S. Khan, and A. Engelbrecht, “Fuzzy Particle Swarm Optimization Algorithms for the Open Shortest Path First Weight Setting Algorithm,” Applied Intelligence, vol. 45, no. 3, pp. 598–621, 2016, doi: https://doi.org/10.1007/s10489-016-0776-0.

[70] K. Harrison, A. Engelbrecht, and B. Ombuki-Berman, “Inertia Control Strategies for Particle Swarm Optimization: Too Much Momentum, Not Enough Analysis,” Swarm Intelligence, vol. 10, no. 4, pp. 267–305, 2016 [Online]. Available: https://link.springer.com/article/10.1007%2Fs11721-016-0128-z

[71] C. Stallmann and A. Engelbrecht, “Gramophone Noise Detection and Reconstruction using Time Delay Artificial Neural Networks,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 47, no. 6, pp. 893–905, 2017, doi: https://doi.org/10.1109/TSMC.2016.2523927.

[72] X. Li, M. Epitropakis, K. Deb, and A. Engelbrecht, “Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications,” IEEE Transactions on Evolutionary Computation, vol. 21, no. 4, pp. 518–538, 2017, doi: https://doi.org/10.1109/TEVC.2016.2638437.

[73] C. Cleghorn and A. Engelbrecht, “Particle Swarm Stability A Theoretical Extension using the Non-Stagnate Distribution Assumption,” Swarm Intelligence, vol. 12, no. 1, pp. 1–22, 2018, doi: https://doi.org/10.1007/s11721-017-0141-x.

[74] J. Maltese, B. Ombuki-Berman, and A. Engelbrecht, “A Scalability Study of Many-Objective Optimization Algorithms,” IEEE Transactions on Evolutionary Computation, vol. 22, no. 1, pp. 79–96, 2018, doi: https://doi.org/10.1109/TEVC.2016.2639360.

[75] K. Harrison, A. Engelbrecht, and B. Ombuki-Berman, “Optimal Parameter Regions and the Time-Dependence of Control Parameter Values for the Particle Swarm Optimization Algorithm,” Swarm and Evolutionary Computation, vol. 41, pp. 20–35, 2018, doi: https://doi.org/10.1016/j.swevo.2018.01.006.

[76] K. Harrison, A. Engelbrecht, and B. Ombuki-Bernman, “Self-Adaptive Particle Swarm Optimization: A Review and Analysis of Convergence,” Swarm Intelligence, vol. 12, pp. 187–226, 2018, doi: https://doi.org/10.1007/s11721-017-0150-9.

[77] A. Bosman, A. Engelbrecht, and M. Helbig, “Fitness Landscapes of Weight-Elimination Neural Networks,” Neural Processing Letters, vol. 48, no. 1, pp. 353–373, 2018, doi: https://doi.org/10.1007/s11063-017-9729-9.

[78] J. Grobler and A. Engelbrecht, “Arithmetic and Parent-Centric Headless Chicken Crossover Operators for Dynamic Particle Swarm Optimization Algorithms,” Soft Computing Journal, vol. 22, no. 18, pp. 5965–5976, 2018, doi: https://doi.org/10.1007/s00500-017-2917-8.

[79] S. van der Stockt and A. Engelbrecht, “Analysis of Selection Hyper-heuristics for Population-based Meta-heuristics in Real-valued Dynamic Optimization,” Swarm Intelligence and Evolutionary Computation, vol. 43, pp. 127–146, 2018, doi: https://doi.org/10.1016/j.swevo.2018.03.012.

[80] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “A Parameter-Free Particle Swarm Optimization Algorithm using Performance Classifiers,” Information Sciences, vol. 503, pp. 381–400, 2019, doi: https://doi.org/10.1016/j.ins.2019.07.016.

[81] A. Engelbrecht, J. Grobler, and J. Langeveld, “Set Based Particle Swarm Optimization for the Feature Selection Problem,” Engineering Applications of Artificial Intelligence, vol. 85, pp. 324–336, 2019, doi: https://doi.org/10.1016/j.engappai.2019.06.008.

[82] M. Nkosi, L. Mamushiane, A. Lysko, A. Engelbrecht, and D. Johnson, “Towards Programmable On-demand Lightpath Services: Current state of the art and open research areas,” IET Network, vol. 8, no. 6, pp. 347–355, 2019, doi: https://doi.org/10.1049/iet-net.2018.5040.

[83] J. Nicholls and A. Engelbrecht, “Co-evolved Genetic Programs for Stock Market Trading,” Journal of Intelligent Systems in Accounting, vol. 26, no. 3, pp. 117–136, 2019, doi: https://doi.org/10.1002/isaf.1458.

[84] E. Oldewage, A. Engelbrecht, and C. Cleghorn, “Degrees of Stochasticity in Particle Swarm Optimization,” Swarm Intelligence, vol. 13, nos. 3 – 4, pp. 193–215, 2019, doi: https://doi.org/10.1007/s11721-019-00168-9.

[85] C. Scheepers and A. Engelbrecht, “Multi-Guide Particle Swarm Optimization for Multi-Objective Optimization,” Swarm Intelligence, vol. 13, nos. 3 – 4, pp. 245–276, 2019, doi: https://doi.org/10.1007/s11721-019-00171-0.

[86] S. Abdulkarim and A. Engelbrecht, “Time Series Forecasting using Neural Networks: Are Recurrent Connections Necessary?” Neural Processing Letters, vol. 50, no. 3, pp. 2763–2795, 2019, doi: https://doi.org/10.1007/s11063-019-10061-5.

2020¶

[87] E. Oldewage, A. Engelbrecht, and C. Cleghorn, “Movement Patterns of a Particle Swarm in High Dimensions,” Information Sciences, vol. 512, pp. 1043–1062, 2020, doi: https://doi.org/10.1016/j.ins.2019.09.057.

[88] C. Dennis, B. Ombuki-Berman, and A. Engelbrecht, “Random Regrouping and Factorization in Cooperative Particle Swarm Optimization Based Large-Scale Neural Network Training,” Neural Processing Letters, vol. 51, no. 1, pp. 759–796, 2020, doi: https://doi.org/10.1007/s11063-019-10112-x.

[89] A. Bosman, A. Engelbrecht, and M. Helbig, “Visualising Basins of Attraction for the Cross-Entropy and the Squared Error Neural Network Loss Functions,” Neurocomputing, vol. 400, pp. 113–136, 2020.

[90] S. Gupta, K. Deep, and A. Engelbrecht, “A robust memory guided sine cosine algorithm for global optimization,” Engineering Applications of Artificial Intelligence, vol. 93, 2020, doi: https://doi.org/10.1016/j.engappai.2020.103718.

[91] A. Kamilaris, F. Prenafeta-Boldú, A. Pitsillides, and A. Engelbrecht, “Transfer of manure as fertilizer from livestock farms to crop fields: The case of catalonia,” Computers and Electronics in Agriculture, vol. 175, 2020, doi: https://doi.org/10.1016/j.compag.2020.105550.

[92] C. Dennis, A. Engelbrecht, and B. Ombuki-Berman, “An analysis of activation function saturation in particle swarm optimization trained neural network,” Neural Processing Letters, 2020, doi: https://doi.org/10.1007/s11063-020-10290-z.

[93] D. Muzo, C. Ruiz, A. Engelbrecht, P. de Miguel Bohoyo, and I. Jiménez, “Visual characterization of hypertensive and diabetic chronic patients based on electronic health records using the self-organizing map,” IEEE ACCESS, vol. 8, pp. 137019–137031, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3012082.

2021¶

[94] R. Nshimirimana, A. Abraham, G. Nothnagel, and A. Engelbrecht, “X-ray and neutron radiography system optimization by means of a multiobjective approach and a simplified ray-tracing method,” Nuclear Technology, 2021, doi: https://doi.org/10.1080/00295450.2020.1740562.

[95] A. Engelbrecht, P. Bosman, and K. Malan, “The influence of fitness landscape characteristics on particle swarm optimisers,” Natural Computing, 2021, doi: https://doi.org/10.1007/s11047-020-09835-x.

[96] J. Mwaura, A. Engelbrecht, and F. Nepomuceno, “Diversity measures for niching algorithms,” Algorithms, vol. 14, no. 2, 2021, doi: https://doi.org/10.3390/a14020036.

[97] R. Lang and A. Engelbrecht, “An exploratory landscape analysis based benchmark suite,” Algorithms, vol. 14, no. 3, 2021, doi: https://doi.org/10.3390/a14030078.

[98] W. Mostert, K. Malan, and A. Engelbrecht, “Performance analysis of feature selection algorithms for automatic algorithms selection,” Algorithms, vol. 14, no. 3, 2021, doi: https://doi.org/10.3390/a14030100.

[99] S. Abdulkarim and A. Engelbrecht, “Time series forecasting with feedforward neural networks trained using particle swarm optimizers for dynamic environments,” Neural Computing and Applications, vol. 33, no. 1, pp. 2667–2683, 2021, doi: https://doi.org/10.1007/s00521-020-05163-4.

[100] M. Ellis, A. Bosman, and A. Engelbrecht, “Characterisation of environment type and difficulty for streamed data classification problems,” Infomation Sciences, vol. 569, pp. 615–649, 2021, doi: https://doi.org/10.1016/j.ins.2021.05.023.

[101] C. Dennis, A. Engelbrecht, and B. Ombuki-Berman, “An analysis of the impact of subsampling on the neural network error surface,” Neurocomputing, vol. 466, pp. 252–264, 2021, doi: https://doi.org/10.1016/j.neucom.2021.09.023.

[102] S. van der Stockt, G. Pampara, A. Engelbrecht, and C. Celghorn, “Performance analysis of dynamic optimization algorithms usinge relative error distance,” Swarm and Evolutionary Computation, vol. 66, 2021, doi: https://doi.org/10.1016/j.swevo.2021.100930.

[103] C. Steenkamp and G. Pampara, “A scalability study of the multi-guide particle swarm optimization algorithm,” Swarm and Evolutionary Computation, vol. 66, 2021, doi: https://doi.org/10.1016/j.swevo.2021.100943.

2022¶

[104] A. Engelbrecht, P. Bosman, and K. Malan, “The influence of fitness landscape characteristics on particle swarm optimisers,” Natural Computing, vol. 21, pp. 335–345, 2022, doi: https://doi.org/10.1007/s11047-020-09835-x.

[105] P. Joócko, B. Ombuki-Berman, and A. Engelbrecht, “Multi-guide particle swarm optimisation archive management strategies for dynamic optimisation problems,” Swarm Intelligence, vol. 16, pp. 143–168, 2022, doi: https://doi.org/10.1007/s11721-022-00210-3.

2023¶

[106] C. Scheepers and A. Engelbrecht, “Comparing Performance of Multi-Objective Algorithms using the Porcupine Measure,” Algorithms, vol. 16, no. 16, 2023, doi: https://doi.org/10.3390/a16060283.

[107] K. Erwin and A. Engelbrecht, “Multi-guide Set-based Particle Swarm Optimization for Multi-objective Portfolio Optimization,” Algorithms, vol. 16, no. 2, 2023, doi: https://doi.org/10.3390/a16020062.

[108] K. Erwin and A. Engelbrecht, “Meta-heuristics for Portfolio Optimization,” Soft Computing, 2023, doi: https://doi.org/10.1007/s00500-023-08177-x.

[109] K. Erwin and A. Engelbrecht, “ Feature-based Complexity Measure for Multinomial Classification Datasets,” Entropy, vol. 25, no. 7, 2023, doi: https://doi.org/10.3390/e25071000.

[110] K. Erwin and A. Engelbrecht, “Meta-heuristics for Portfolio Optimization,” Soft Computing, 2023, doi: https://doi.org/10.1007/s00500-023-08177-x.

[111] L. Hayward and A. Engelbrecht, “Determining Meta-Heuristic Similarity Using Behavioral Analysis,” IEEE Transactions on Evolutionary Computation, 2023, doi: https://doi.org/10.1109/TEVC.2023.3346672.

[112] M. Helbig and A. Engelbrecht, “Solving Many-objective Optimisation Problems using Partial Dominance,” Neural Computing with Applications, 2023, doi: https://doi.org/10.1007/s00521-023-09145-0.

[113] A. Madani, A. Engelbrecht, and B. Ombuki-Berman, “Cooperative Coevolutionary Multi-Guide Particle Swarm Optimization Algorithm for Large-Scale Multi-Objective Optimization Problems,” Swarm and Evolutionary Computation, vol. 78, 2023, doi: https://doi.org/10.1016/j.swevo.2023.101262.

[114] A. McNulty, B. Ombuki-Berman, and A. Engelbrecht, “Decomposition and Merging Co-operative Particle Swarm Optimization with Random Grouping for Large-Scale Optimization Problems,” Swarm Intelligence, 2023, doi: https://doi.org/10.1007/s11721-023-00229-0.

[115] K. Parsopoulos and A. Engelbrecht, “Particle Swarm Optimization and Differential Evolution,” Encyclopedia of Optimization, 2023, doi: https://doi.org/10.1007/978-3-030-54621-2_839-1.

[116] J. van Zyl and A. Engelbrecht, “ Set-based Particle Swarm Optimisation: A Review,” Swarm and Evolutionary Computation, vol. 78, 2023, doi: https://doi.org/10.1016/j.swevo.2023.101262.

2024¶

[117] M. Omran and A. Engelbrecht, “Time Complexity of Population-Based Metaheuristics,” Mendel Soft Computing Journal, 2024, doi: https://doi.org/10.13164/mendel.2023.2.255.

[118] D. von Eschwege and A. Engelbrecht, “Belief Space Guided Approach to Self-Adaptive Particle Swarm Optimization,” Swarm Intelligence, 2024, doi: https://doi.org/10.1007/s11721-023-00232-5.