IEEE WCCI 2026 & GECCO 2026 (Joint Competition)
June 08 – June 12 – Hangzhou, China (CEC 2025) | July 14 – July 18 – Málaga, Spain (GECCO 2025)
Organized by Polytechnic of Porto (ISEP)
Polytechnic of Porto (ISEP): Fernando Lezama, José Almeida, João Soares, Filipe Sousa, Zita Vale
Important: The use of the software platform is prohibited for purposes other than competition without a prior authorization of the organizing team.
Results Published: (TBD)
Registration
The registration of participants is mandatory and must be made online using the following form:
Note: If you are unable to complete this form, please send it by email to: flz@isep.ipp.pt; jorga@isep.ipp.pt; jan@isep.ipp.pt; ffeso@isep.ipp.pt; zav@isep.ipp.pt
Final Rank Results
Results Published: (TBD)
Competition Outline
Following the success of the previous editions at IEEE PES-GM, CEC, GECCO, and WCCI, we are launching another challenging edition of the competition at major conferences in the field of computational intelligence and power systems. This WCCI and GECCO 2026 competition challenges participants to develop benchmark evolutionary algorithms for optimizing energy pricing strategies in Local Energy Communities (LECs). Unlike traditional methods focused solely on cost minimization, this competition emphasizes fairness-aware pricing, where fairness metrics (e.g., Jain’s Fairness index, standard deviation of energy bills, Quality of Experience) guide the optimization process. The goal is to promote the design of metaheuristic algorithms that produce pricing strategies balancing technical efficiency and equitable cost distribution among community members.
Note: The track is developed to run under the same framework as past competitions.
Competition Goals
The competition has been held since 2017 at major conferences (the first competition was launched at IEEE PES GM) – Website: http://www.gecad.isep.ipp.pt/smartgridcompetitions.
We have been implementing variants of the benchmark problems, but have always used the same framework. This year, we implemented a novel framework for fairness-aware pricing optimization in energy communities (a relatively recent problem in the energy domain), using an evaluation criterion that includes the fitness value and the number of function evaluations (e.g., convergence).
Rules
- Participants will propose and implement metaheuristic algorithms (e.g., evolutionary algorithms, swarm intelligence, estimation of distribution algorithm, etc.) to solve the proposed track problem in the energy domain.
- The organizers provide a framework (Download), implemented in MATALAB© 2021a 64 bits, in which participants can easily test their algorithms (we also provide a hybrid-adaptive differential evolution algorithm implementation as an example). The guidelines (Download) include the necessary information to understand the problem, how the solutions are represented, and how the fitness function is evaluated. Also, we provide information on mathematical formulation regarding the objective function value and problem constraints. Those elements are common for all participants.
- A maximum number of “function evaluations” is considered for all algorithms. However, this year, the algorithms’ convergence properties, measured as the number of “functions evaluations”, are part of the evaluation criteria in the competition. Thus, participants should strive to obtain the lowest number of “function evaluations” as well.
- In addition, 20 independent trials should be performed in the framework by each participant.
How to Submit an Entry
– The winner will be the participant with the minimum ranking index in the proposed track, which is calculated as the sum of the normalized values of the average fitness value and the average number of function evaluations for the 20 trials. Possible outliers in the normalization will be handled by winsorizing the results.
– Each participant is kindly requested to put the text files corresponding to final results (see guidelines document), as well as the implementation files (codes), obtained by using a specific optimizer, into a zipped folder named GECCO_WCCI_2026_AlgorithmName_ParticipantName.zip
(e.g. GECCO_WCCI_2026_DE_JoseAlmeida.zip).
The participants should submit the files to the registration form (link) by 1st June 2026 (anywhere on Earth).
Important Remarks
- Notice that submission of papers or assistance to CEC and GECCO by competition participants is not mandatory.
- You can submit a paper to the WCCI-2026 Special Session on User-Centric Algorithms and Fair Applications using AI and Evolutionary Computation (Submit it here) – select Special Session: User-Centric Algorithms and Fair Applications using AI and Evolutionary Computation.
- You are also welcome to submit short descriptions of your algorithms and results as 2-page papers to be included in the GECCO Companion. This is voluntary: the submission deadline is TBD. Submit it here (Competition Entry Submissions)
IEEE CES PRIZE: (TBD)
Submit your results by 1st June 2026 (anywhere on Earth)
Further Related Bibliography
- [1] F., Lezama, J. Soares, Z. Vale, J. Rueda, S. Rivera, & I. Elrich, 2017 IEEE Competition on modern heuristic optimizers for smart grid operation: Testbeds and results. Swarm and evolutionary computation, 44, 420-427, 2019.
- [2] F. Lezama, J. Soares, P. Hernandez-Leal, M. Kaisers, T. Pinto, and Z. Vale, Local Energy Markets: Paving the Path Towards Fully Transactive Energy Systems, IEEE Transaction on Power Systems, IEEE (2018).
- [4] F. Lezama, J. Soares, E. Munoz de Cote, L. E. Sucar, and Z. Vale, “Differential Evolution Strategies for Large-Scale Energy Resource Management in Smart Grids,” in GECCO ’17: Genetic and Evolutionary Computation Conference Companion Proceedings, 2017.
- [5] J. Almeida, J. Soares, F. Lezama, S. Limmer, T. Rodemann, and Z. Vale, “A systematic review of explainability in computational intelligence for optimization,” Computer Science Review, vol. 57, p. 100764, Aug. 2025.
- [6] J. Soares et al., “Review on fairness in local energy systems,” Applied Energy, vol. 374, p. 123933, Nov. 2024.
- [7] F. Lezama, F. Doria, and J. Soares, “Fair Pricing Optimization in Energy Communities with Differential Evolution,” in Proceedings of the International Workshop on AI Systems for the Environment (AISE-2025), 2025, pp. 61–70.
- [8] J. Almeida, J. Soares, E. Aliyan, S. Limmer, R. Faia, and S. Ramos, “Fairness Index Analysis in Local Energy Communities Considering Electric Vehicles and Energy Storage,” in Proceedings of the VII Ibero-American Congress of Smart Cities, ICSC-Cities 2024, 12–14 November, San Carlos, Costa Rica, D. Rossit, P. Moreno-Bernal, and C. E. Torres-Aguilar, Eds., Singapore: Springer Nature Singapore, 2025, pp. 175–189.
Organizers
