Publications

Preparation/submitted/under revision

Cooperative Coevolutionary Multi-guide Particle Swarm Optimization Algorithm for Large-Scale Multi-Objective Optimization
Amirali Madani, Andries .P Engelbrech and Beatrice M. Ombuki-Berman
Landscape Aware Algorithm Configuration
Cody Dennis, Beatrice M. Ombuki-Berman and Andries P Engelbrecht
Quantum Multi-guide Particle Swarm Optimization for Dynamic Multi-objective Optimization Problems
Jocko Pawel, Beatrice M. Ombuki-Berman and Andries P Engelbrecht

Refereed Publications (Journals, conference proceedings and book chapter)

A Particle Swarm Optimization Decomposition Strategy for Large Scale Global Optimization
Liam McDevitt, Beatrice M. Ombuki-Berman and Andries P Engelbrecht
IEEE Symposium Series on Computation Intelligence, IEEE SSCI 2022, Singapore, Accepted, December 2022.
Cooperative Particle Swarm Optimization Decomposition Methods for Large-scale Optimization
Mitchell D. Clark, Beatrice M. Ombuki-Berman, Nicholas Aksamit and Andries .P Engelbrecht
IEEE Symposium Series on Computation Intelligencee, IEEE SSCI 2022, Singapore, Accepted, December 2022.
Decomposition and Merging Co-operative Particle Swarm Optimization with Random Grouping
Alanna McNulty, B. M. Ombuki-Berman and A. P. Engelbrecht
13th International Conference on Swarm Intelligence, ANTS 2022, Malaga, Spain, Accepted, November, 2022.
Cooperative Multi-objective Particle Swarm Optimization and Differential Evolution for Drug Design
Nicholas Aksamit, B. M. Ombuki-Berman and Yifeng Li
19th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biologye, IEEE CIBCB 2022, Extended 2-page abstract, Ottawa, Canada, Augus, 2022.
Dynamic Multi-objective Optimisation Using Multi-guide Particle Swarm Optimisation
Jocko Pawel, B. M. Ombuki-Berman and A. P. Engelbrecht
at World Congress on Computational Intelligence,
IEEE 2022 IEEE CEC Congress of Evolutionary Computation , IEEE CEC 2022, Padova, Italy, Accepted, July, 2022.
Multi-guide Particle Swarm Optimization Archive Management Strategies for Dynamic Optimization Problems
Jocko Pawel, Beatrice M. Ombuki-Berman and Andries P Engelbrecht
Swarm Intelligence (Springer), 16:143-168, February, 2022.
An analysis of the impact of subsampling on the neural network error surface
Cody Dennis, Andries Engelbrecht and Beatrice M. Ombuki-Berman
Neurocomputing (Elsevier), 466:252-264, November 2021.
Visualizing and characterizing the parameter configuration landscape of Particle Swarm Optimization using physical landform classification
K. R. Harrison, B. M. Ombuki-Berman and A. P. Engelbrecht
2021 IEEE CEC Congress of Evolutionary Computation , CEC 2021, pp.2299-2306, Krakow, Poland, June 2021.
Decision Space Scalability Analysis of Multi-objective Particle Swarm Optimization Algorithms
A. Madani, B. M. Ombuki-Berman and A.P Engelbrecht
2021 IEEE CEC Congress of Evolutionary Computation , CEC 2021, pp.2179-2186, Krakow, Poland, June 2021.
Predicting particle swarm control parameters from fitness landscape characteristics
C. Dennis, B. M. Ombuki-Berman and A.P Engelbrecht
2021 IEEE CEC Congress of Evolutionary Computation , CEC 2021, pp.2289-2298, Krakow, Poland, June 2021.
T. Crane, A. P. Engelbrecht and B. M. Ombuki-Berman
12th International Conference on Swarm Intelligence (ICSI’21), Qingdao, China, to Appear in Springer-Nature Lecture Notes (LNCS) in Computer Scienc, Accepted MArch 2021.
Visualizing and characterizing the parameter configuration landscape of differential evolution using physical landform classification
K. R. Harrison, B. M. Ombuki-Berman and A. P. Engelbrecht
2020 IEEE Symposium Series on Computational Intelligence , IEEE, Canberra, Australia, pp.2437- 2444, Devember 2020.
NichePSO and the Merging Subswarm Problem
T. Crane, B. Ombuki-Berman and A. Engelbrecht
2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI) , Stockholm, Sweden,pp. 17-22, November 2020.
A Hybrid Approach to Network Robustness optimization using edge rewiring and edge Addition
J. Paterson and B. M. Ombuki-Berman 2020 IEEE International Conference on Systems, Man and Cybernetics , IEEE SMC 2020, Toronto, pp. 4051- 4057, October 2020.
Swarm Based Algorithms for Neural Network Training
R. McLean, B. M. Ombuki-Berman and A.P Engelbrecht
2020 IEEE International Conference on Systems, Man and Cybernetics IEEE SMC 2020, Toronto, pp. 2585- 2592, October 2020,
An Analysis of Activation Function Saturation in Particle Swarm Optimization Trained Neural Networks Training
C. Dennis, A.P Engelbrecht and B. M. Ombuki-Berman
Neural Processing Letters , 52:1123-1153, September 2020.
Juan C. Burguillo: Self-organizing coalitions for managing complexity
Ombuki-Berman B.
Genetic Programming and Evolvable Machines (2020).
https://doi.org/10.1007/s10710-019-09372-2. Invited Book Review.
Random Regrouping and Factorization in Cooperative Particle Swarm Optimization Based Large-Scale Neural Network Training
Cody Dennis, Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht
Neural Processing Letters 51(1), 759-796, 2020 , DOI 10.1007/s11063-019-10112-x
A Parameter-Free Particle Swarm Optimization Algorithm using Performance Classifiers
K.R.Harrison,B.M.Ombuki-Berman,and A.P.Engelbrecht
Information Sciences vol. 503, pp.381- 400, 2019.
The Parameter Configuration Landscape: A Case Study on Particle Swarm Optimization
K. R. Harrison, B. M. Ombuki-Berman, and A. P. Engelbrecht
IEEE Congress on Evolutionary Computation (CEC 2019) , pp. 808-814, 2019.
An Analysis of Control Parameter Importance in the Particle Swarm Optimization Algorithm
K.R.Harrison, B.M.Ombuki-Berman, and A.P.Engelbrecht
In Advances in Swarm Intelligence, , Y. Tan, Y. Shi, and B. Niu, Eds., Springer International Publishing, pp. pp. 93-105, 2019.
Optimizing Scale-Free Network Robustness with the Great Deluge Algorithm
J. Paterson and B. M. Ombuki-Berman
The 31st International Conference on Industrial, Engineering & Other applications of Applied Intelligent Systems,
IEA-AIE 2018 , Accepted, Montreal, June 2018.
Optimal Parameter Regions and the Time Dependence of Control Parameter Values
for the Particle Swarm Optimization Algorithm
K.R Harrison, A.P Engelbrecht and B. M. Ombuki-Berman
Swarm and Evolutionary Computation 41 , pp. 20-35, Elsevier, 2018.
Merging and Decomposition Variants of Cooperative Particle Swarm Optimization:
New Algorithms for Large Scale Optimization Problems
Jay Douglas, Andries Engelbrecht and Beatrice Ombuki-Berman
2018 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence ,
ISMSI2018, Accepted Phuket, Thailand, March 2018.
Gaussian-Valued Particle Swarm Optimization
K.R Harrison, B. M Ombuki-Berman and A.P Engelbrecht
in Swarm Intelligence , M. Dorigo, M. Birattari, C. Blum, A.L Christensen, A. Reina, and V. Trianni, Eds, Springer International Publishing, pp. 368- 377, 2018.
A Bi-Objective Critical Node Detection Problem
M. Venntresca, K. Harrison, and B.M. Ombuki-Berman
European Journal of Operational Research, 254(3):895-908, March 2018.
A Scalability Study of Many-Objective Optimization Algorithms
Justin Maltese, Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht.
IEEE Transactions on Evolutionary Computation, pp: 79-96, February 2018.
Self-Adaptive Particle Swarm Optimization: A review and Analysis of Convergence
K.R Harrison, A.P Engelbrecht, and B. M Ombuki-Berman
Swarm Intelligence , 12(3) pp. 187–226, Springer, 2018.
An Age Layered Population Structure Genetic Algorithm for Multi-Depot Vehicle Routing
Audrey Opoku-Amankwaar and B.M Ombuki
2017 IEEE Symposium Series on Computation Intelligence , pp.3403-3410, Hawai, November 2017.
Optimal Parameter Regions for Particle Swarm Optimization Algorithms
Kyle R. Harrison Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht.
IEEE Congress on Evolutionary Computation, CEC 2017 pp. 349-356, Spain, June 2017.
Inertia weight control strategies for particle swarm optimization
Kyle R. Harrison, Andries P. Engelbrecht and Beatrice M. Ombuki-Berman.
Swarm Intelligence , Volume 10, Issue 4, pp:267-305, December 2016.
A Meta-Analysis of Centrality Measures for Comparing and Generating Complex Network Models.
Kyle Robert Harrison, Mario Ventresca, and Beatrice M. Ombuki-Berman.
Journal of Computational Science, Elsevier, 17(1):205-215, November 2016.
Automatic Inference of Graph Models for Directed Complex Networks using Genetic Programming
Michael Medland, Kyle Robert Harrison, and Beatrice M. Ombuki-Berman.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 2337-2344, Vancouver, July 2016.
Pareto-Based Many-Objective Optimization using Knee Points
Justin Maltese, Beatrice M. Ombuki-Berman, and Andries P. Engelbrecht.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. pp. 3678 - 3686, Vancouver, July 2016.
The Sad State of Self-Adaptive Particle Swarm Optimizers
Kyle R. Harrison, Andries P. Engelbrecht and Beatrice M. Ombuki-Berman.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 431-439, Vancouver, July 2016.
A Radius-Free Quantum Particle Swarm Optimization Technique for Dynamic Optimization Problems
K.R. Harrison, B.M Ommbuki-Berman, and Andries P. Engelbrecht.
IEEE Congress on Evolutionary Computation, IEEE CEC 2016, pp. 578-585, Vancouver, July 2016.
High-Dimensional Multi-objective Optimization Using Co-operative Vector-Evaluated Particle Swarm Optimization With Random Variable Grouping.
Justin Maltese, Andries P. Engelbrecht and Beatrice M. Ombuki.
2015 IEEE Symposium on Swarm Intelligence. 1302 - 1309, Cape Town, SA, December 2015.
The Effect of Probability Distributions on the Performance of Quantum Particle Swarm Optimization for Solving Dynamic Optimization Problems.
Kyle Harrison, Beatrice Ombuki-Berman and Andries P. Engelbrecht.
2015 IEEE Symposium on Swarm Intelligence. 242 - 250, Cape Town, SA, December 2015.
Cooperative Vector Evaluated Particle Swarm Optimization for Multi-objective Optimization.
Justin Maltese, Beatrice Ombuki-Berman and Andries P. Engelbrecht.
2015 IEEE Symposium on Swarm Intelligence. pp. 1294 - 1301, Cape Town, SA, December 2015.
A GA Approach for finding decision rules based on bireducts
Oleg Rybik, Ivo Duntsch and Beatrice Ombuki-Berman
Extended abstract, RST 2015, Warsaw, Poland, June 2015.
Evaluating Landscape Characteristics of Dynamic Benchmark Functions.
Ron Bond, Andries Engelbrecht and Beatrice Ombuki-Berman.
2015 IEEE Congress on Evolutionary Computation, CEC 2015, (CEC 2015), pp. 187 - 195, Sendai, Japan, May 2015.
Vector-Evaluated Particle Swarm Optimization with Local Search.
Derek Dibblee, Justin Maltese, Beatrice Ombuki-Berman and Andries Engelbrecht.
2015 IEEE Congress on Evolutionary Computation, CEC 2015, (CEC 2015), pp. 1343 - 1350, Sendai, Japan, May 2015.
Investigating Fitness Measures for the Automatic Construction of Graph Models.
Kyle Harrison, Mario Ventresca and Beatrice Ombuki-Berman.
Lecture Notes in Computer Science , 9028 pp:189-200, 2015
18th European Conference on the Applications of Evolutionary Computation , EvoCOMPLEX, Copenhagen, Denmark, April, 2015.
An Experimental Evaluation of Multi-Objective Evolutionary Algorithms for Detecting Critical Nodes in Complex Networks.
Mario Ventresca, Kyle Harrison, and Beatrice Ombuki-Berman.
Lecture Notes in Computer Science , 9028 pp:164-176,2015.
18th European Conference on the Applications of Evolutionary Computation , EvoCOMPLEX, Copenhagen, Denmark, April 2015.
Demonstrating the Power of Object-Oriented Genetic Programming via the Inference of Graph Models for Complex Networks.
Michael Medland, Kyle Harrison and Beatrice Ombuki-Berman.
6th World Congress on Nature and Biologically Inspired Computing, (NABIC 2014), pp. pp. 305-311, Portugal, August 2014.
Dynamic Multi-Objective Optimization using Charged Vector Evaluated Particle Swarm Optimization.
Kyle Harrison, Beatrice Ombuki-Berman and Andries Engelbrecht.
IEEE Conference on Evolutionary Computation, (CEC 2014), pp. 1929 - 1936, Beijing, China, July 2014.
Incorporating Expert Knowledge in Object-Oriented Genetic Programming.
Michael Medland, Kyle Harrison and Beatrice Ombuki-Berman.
Genetic and Evolutionary Computation Conference, GECCO 2014, pp. 145 - 146, Vancouver, July 2014.
Genetic Programming for the Automatic Inference of Graph Models for Complex Networks.
A. Bailey, M. Ventresca and B.Ombuki-Berman.
IEEE Transactions on Evolutionary Computation , 18(3):405-419, June, 2014.
Automatic Inference of Hierarchical Graph Models using Genetic Programming with an Application to Cortical Networks.
A. Bailey, B. Ombuki-Berman and M. Ventresca.
Genetic and Evolutionary Computation Conference, GECCO 2013 , pp.893-900, Amsterdam, July 2013.
A Scalability Study of Multi-Objective Particle Swarm Optimizers.
K. Harrison, A.P. Engelbrecht and B.Ombuki-Berman
2013 IEEE Conference on Evolutionary Computation (CEC 2013), , pp.189-197, Mexico, June 2013.
Cooperative Particle Swarm Optimization for Dynamic Environments.
N. Unger, B. Ombuki-Berman and A. P. Engelbrecht.
2013 IEEE Symposium on Swarm Intelligence. pp. 172-179, Singapore, April 2013.
Discrete Particle Swarm Optimization for the Single Allocation Hub Location Problem.
A. Bailey, B. Ombuki-Berman and S. Asobiela
2013 IEEE Symposium Series on Computational Intelligence. pp.92-98, Singapore, April 2013.
Predicting GAs Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures. ,
M. Ventresca, B. Ombuki-Berman and A. Runka.
13th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP), pp. 214-225, Vienna, Austria, April 2013.
Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization.
K. Harrison, B. Ombuki-Berman and A. Engelbrecht.
Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science Volume 7811, 2013, pp. 171-184 , March 2013.
Automatic Generation of Graph Models for Complex Networks using Genetic Programming,
A. Bailey, M. Ventresca, and B. Ombuki-Berman.
Genetic and Evolutionary Computation Conference, GECCO 2012 , pp.711-718, Philadelphia, USA, July 2012.
Using Summed Ranks and Pareto Ranking to Design Outpatient Schedules,
Ombuki-Berman, B., Klassen, K. and Harrington, A.
Canadian Operational Research Society Conference , Niagara Falls, Ontario, June, 2012.
Developing Appointment Schedules with Genetic Algorithms,
Klassen, K., Ombuki-Berman, B. and Harrington, A..
Production and Operations Management Society Conference , Chicago, Illinois, April, 2012.
An Efficient Genetic Algorithm for the Uncapacitated Single Allocation Hub Location Problem.
M. Naeem and B. Ombuki-Berman.
2010 IEEE Conference on Evolutionary Computation (CEC 2010) , pp.941-948, Barcelona, Spain, July 18-23, 2010.
A Search Space Analysis for the Waste Collection Vehicle Routing Problem with Time Windows.
A. Runka, B. Ombuki-Berman, M. Ventresca.
Refereed Poster Paper, GECCO 2009 , Montreal, July 2009.
Using genetic algorithms for multi-depot vehicle routing.
B. Ombuki-Berman and F. Hanshar.
F.B. Pereira, J.Tavares (Eds.) in, Bio-Inspired Algorithms for the Vehicle Routing Problem, Springer-Studies in Computational Intelligence, v.161, pp:77-99, 2009.
Genetic Algorithm Cryptanalysis of Substitution Permutation Network.
J. Brown, S. Houghten and B. Ombuki-Berman.
IEEE Symposium on Computational Intelligence in Cyber Security . pp:115-121, Nashville, Tennessee, USA, 2009.
Particle swarm optimization for the Design of Two Connected Networks with Bounded Rings.
E. B. Foxwell and B. Ombuki-Berman.
Journal of High Performance System Architecture , Vol. 1. no.4, pp. 220-230, 2008.
(Initial results presented at Workshop on Parallel Architectures and Bioinspired Algorithms, pp:21-29, Toronto, Oct. 2008)
Dynamic Vehicle Routing using Genetic Algorithm.
F.T. Hanshar and B. Ombuki-Berman.
Applied Intelligence, 27(1):89-99, August 2007.
Waste collection vehicle routing problem with time windows using multi-objective genetic algorithms .
B. Ombuki-Berman, A. Runka and F. Hanshar.
Computational Intelligence (CI 2007) , Banff, Canada, July 2007.
Search Difficulty of Two-Connected Ring-based Topological Network Designs.
B. Ombuki-Berman, M. Ventresca.
IEEE Symposium on Foundations of Computational Intelligence (FOCI), pp:267-274, Honolulu, USA, April 2007.
Epistasis in Multi-Objective Evolutionary Recurrent Neuro-Controllers.
M. Ventresca, B. Ombuki-Berman.
IEEE Symposium on Artificial Life (CI-ALIFE), pp:77-84, Honolulu, USA, April 2007.
Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks.
M. Ventresca and B. M. Ombuki.
IEEE International Joint Conference on Neural Networks (IJCNN), pp:8947-8954, Vancouver, Canada, July 2006.
Multi-Objective Genetic Algorithms for Vehicle Routing Problems with Time Windows.
B. Ombuki, Brian J. Ross and F. Hanshar.
Applied Intelligence, 24(1):17-30, February 2006.
A Genetic Algorithm for the Design of Two Connected Networks with Bounded Rings.
M. Ventresca and B. Ombuki.
Computational Intelligence and Applications, Special Issue on Nature-Inspired Approaches to Networks and Telecommunications, 5(2):267-281, November 2005.
Ant Colony Optimization for Job Shop Scheduling Problem.
M. Ventresca and B. M. Ombuki.
Proceedings of 8th IASTED Intl. Conf. On Artificial Intelligence and Soft Computing, (ASC 2004), CDROM.
451-152. Marbella,Spain, ed. A.P.del Pobil, ACTA Press, September 2004.
A Genetic Algorithm for the Design of Two Connected Networks with Bounded Rings.
M. Ventresca and B. Ombuki.
Workshop on Nature Inspired Approaches to Networks and Telecommunications workshop at the 8th International Conference on Parallel Problem Solving from Nature (PPSN VIII), Birmingham, UK, on 18-22 September, 2004.
Local Search Genetic Algorithm for the Job Shop Scheduling Problem.
B. Ombuki and M. Ventresca.
Applied Intelligence 21 (1): 99-109, July 2004.
Meta-heuristics for the Job Shop Scheduling Problem. M. Ventresca and B. Ombuki. Proceedings of Late
Breaking Papers, Genetic and Evolutionary Computation Conference M. Ventresca and B.M. Ombuki.
(GECCO-2003) pp.303-306, Chicago, 2003.
A Hybrid Search Based on Genetic Algorithms and Tabu Search for Vehicle Routing.
B. Ombuki, M. Nakamura & M. Osamu.
6th International Conference on Artificial Intelligence and Soft Computing , pp. 176-181, Banff, Canada, July 2002.
An Evolutionary Algorithm Approach to the Design of Minimum Cost Survivable Networks with Bounded Rings. B. Ombuki, M. Nakamura & M. Osamu.
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E84-A No.6 pp.1545-1548, June 2001.
Cyclic job-shop-scheduling basedon evolutionary Petri nets. Nakamura, M., Tome, H., Hachiman, K., Ombuki, B.M. and Onaga, K.
Industrial Electronics Society, 2000. 26th Annual Conference of the IEEE,
IECON 2000 , Vol. 4, pp. 2855-2860, 2000, Nagoya, Japan.
A Genetic Algorithm Approach for the Design of Minimum Cost Survivable Networks With Bounded Rings.
B. Ombuki, M. Nakamura, Z. Nakao and K. Onaga.
Proceedings of International Technical Conference On Circuits/Systems, Computers And Communications, ITC-CSSS'00 , Vol. 1,pp 493-496, 2000, Pusan, Korea.
A Flexible Routing based on Object Oriented GAs in Vehicle Routing Problem with Time Constraints.
Khan S., Nakamura M., Ombuki B.M and Onaga K.
Proceedings of the IEICE General Conference (Institute of Electronics, Information and Communication Engineers) , VOL.2000;NO.;PAGE.252(2000).
Evolutionary Computation for Topological Optimization of 3-Connected Computer Networks.
B. Ombuki, M. Nakamura, Z. Nakao and K.
Onaga.IEEE International Conference on Systems, Man, and Cybernetics , CD, Tokyo, Oct. 1999
A Genetic Algorithm Approach to Vehicle RoutingProblem with Time Deadlines in Geographical Information Systems.
O. Maeda, M.Nakamura, B. Ombuki, and K. Onaga.
IEEE International Conference on Systems, Man, and Cybernetics, Vol.II, pp.595-600, Tokyo, Oct. 1999.
Experimental Evaluation of an Evolutionary Scheduling Scheme based on gkGA Approach to the Job Shop Problem.
B. Ombuki, M. Nakamura, and K. Onaga.
Second International Conference on Knowledge-Based Intelligent Electronic Systems , Vol. 3, pp 197-201, Adlaide, Australia, April 1998.
An Evolutionary Scheduling Scheme based on gkGA Approach to the Job Shop Problem.
B. Ombuki, M. Nakamura, and K. Onaga.
IEICE Transactions on Fundamentals of Electronics, Communication and
Computer Sciences, Vol. E pp 1063-1071, June 1998.
A New Hybrid GA Solution to Combinatorial Optimization Problems - An Application to the Multiprocessor Scheduling Problems.
Nakamura, B. Ombuki, K. Shimabukuro, and K. Onaga.
Journal for Artificial Life and Robotics, Springer Verlag, Vol.2, 74-79, 1998.
A Hybridized GA Approach to the Job Shop Problem.
B. Ombuki, M. Nakamura, and K. Onaga.
Proceedings of International Technical Conference On Circuits/Systems, Computers
and Communications, ITC-CSSS'97, Vol. 1,pp 483-486, Okinawa, Japan.