iMOPSE (Intelligent Multi Objective Project Scheduling Environment) is a project which aims to provide the tools that allow users to optimize various schedules using a variety of heuristics or metaheuristics, such as Evolutionary Algorithm, Differential Evolution, Tabu Search, Simulated Annealing, Particle Swarm Optimization, Ant Colony Optimization and others.
In iMOPSE a Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is defined and benchmark dataset to support automated solvers. The MS-RCPSP problem can be defined as one objective optimisation (e.g. cost or makespan), multi objective optimisation (eg. cost and makespan) or many-objecive optimisation (5 objectives have been defined).
Fig. 1. Example of 5-objective optimisation for small instance (10_7_10_7_2).
Our recent works are connected with niching methods in multi modal optimisation problems, where NTGA2 adaptation (called GaMeDe) proved to be very effective (2nd price -- GECCO 2020 Competition on Niching Methods for Multimodal Optimization).
Example of multimodal optimisation GaMeDe results (CEC2013, problem 13) [ details ]
NEWS
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Last update [03.07.2023]
new paper |
Myszkowski P.B., Antkiewicz M., "Hardness and constrainedness in multi–objective Multi-Skill Resource-Constrained Project Scheduling Problem – a case study", Submitted to: European Journal of Operational Research |
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Last update [24.05.2023]
new paper |
Antkiewicz M., Myszkowski P.B., "Population-less Genetic Algorithm? Investigation of Non-dominated Tournament Genetic Algorithm (NTGA2) for multi-objective optimization", Submitted to: 16th International Workshop on Computational Optimization (WCO’23) details and extra materials: download FILE (all approximated Pareto fronts, ~363KB). |
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Last update [14.07.2022]
new paper |
Myszkowski P.B., Laszczyk M., "Investigation of benchmark dataset for many–objective Multi–Skill Resource Constrained Project Scheduling Problem", Full investigation of MS-RCPSP as 5-objective optimisation problem (many--objective optimisation). details and extra meterials: here. |
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Update [10.03.2021]
best paper |
GaMeDe method is publised in the article:
Laszczyk M., Myszkowski P., "A Gap–based Memetic Differential Evolution (GaMeDE) applied to multi–modal optimisation – using multi–objective optimization concepts", ACIIDS 2021 (best student paper). 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021. DOI: https://doi.org/10.1007/978-3-030-73280-6_17 |
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Update [01.02.2021]
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Paper:
Myszkowski P.B., Laszczyk M.,
"Diversity based selection for many-objective evolutionary optimisation problems with constraints",
has been published to Information Sciences 546 (2021) 665–700 MS-RCPSP has been extended to 5-objective optimisation problem (many--objective optimisation) and new NTGA2 method has been investigated (in comparisions to NTGA, Theta-DEA, U-NSGAIII). DOI: https://doi.org/10.1016/j.ins.2020.08.118. |
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Update [20.07.2020]
GECCO 2nd price |
GaMeDe method -- based on NTGA2 orginally defined to solve multi-ojbective MS-RCPSP -- has achieved 2rd place in Niching in MultiModal Optmimisation Competition (GECCO20)
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Update [04.09.2019]
best paper |
Paper: "A Specialized Evolutionary Approach to the bi-objective Travelling Thief Problem" by Maciej Laszczyk, Paweł B. Myszkowski (DOI: http://dx.doi.org/10.15439/2019F191) is the best student paper in 14th International Symposium Advances in Artificial Intelligence and Applications (AAIA'19) Details here. |
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Update [20.07.2019]
GECCO 3rd price |
NTGA method achieved 3rd place in (GECCO2019) - Bi-objective Traveling Thief Competition.
GECCO'19 certificate: 
Details here. |
Wrocław University of Science and Technology