{"id":96,"date":"2019-10-02T15:01:40","date_gmt":"2019-10-02T15:01:40","guid":{"rendered":"http:\/\/www.gecad.isep.ipp.pt\/ERM-competitions\/?page_id=96"},"modified":"2023-12-14T10:36:34","modified_gmt":"2023-12-14T10:36:34","slug":"2020-2","status":"publish","type":"page","link":"https:\/\/www.gecad.isep.ipp.pt\/ERM-competitions\/2020-2\/","title":{"rendered":"Call for Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications"},"content":{"rendered":"\n

IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020<\/a> <\/p>\n\n\n\n

GECCO 2020 @ Cancun – GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE<\/a><\/p>\n\n\n\n

8th – 12th July 2020, Canc\u00fan (Mexico) & 19th – 24th July 2020, Glasgow (UK)<\/h4>\n\n\n\n

Organized<\/strong> by Joao Soares, Fernando Lezama, Bruno Canizes, Zita Vale<\/p>\n\n\n\n

Final Rank Results<\/h2>\n\n\n\n
Final Rank<\/strong><\/td>Name<\/strong><\/td>Affiliation, Country<\/strong><\/td>Algorithm<\/strong><\/td>Final Rank<\/strong><\/td><\/tr>
1
IEEE CIS Prize – 500 $<\/span><\/strong><\/td>		Ansel Y. Rodriguez-Gonzalez,
Bryan Rodrigo Quiroz Palominos,
Carlos A. Oliva Moreno,
Yoan Martinez Lopez,
Julio Madera<\/td>		CISECE-UT3, Mexico; UC, Cuba; UCLV, Cuba<\/td>CUMDANCauchy++: a Cellular EDA

Presentation (link)<\/a>
Algorithm (link)<\/a><\/td>		5.48<\/td><\/tr>
2<\/td>
Yoan Mart\u00ednez L\u00f3pez, 
Julio Madera Quintana,
Alexis Moya,
Miguel B\u00e9thencourt,
Ansel Rodr\u00edguez Gonz\u00e1lez<\/td>
UC, Cuba; UCLV, Cuba;
CICESE -UT3, Mexico<\/td>		DEEDA

Presentation (link)
<\/a>Algorithm (link)<\/a><\/td>		5.41<\/td><\/tr>
3<\/td>
Diego Rodriguez,
Julian Garcia, 
David Alvarez,
Jose Gil, 
Sergio Rivera,<\/td>
UNAC & EMGEA-ENEL, Colombia<\/td>
Ensembled method of CBBO, Cauchy and DEEPSO algorithm

Algorithm (link)<\/a><\/td>		5.34<\/td><\/tr>
4<\/td>
Deunsol Yoon<\/td>		KAIST, Korea<\/td>
RDG3+DEEPSO

Presentation (link)
<\/a>Algorithm (link)<\/a><\/td>		5.00<\/td><\/tr>
5<\/td>
Wenlei Bai<\/td>
ABB Enterprise Software Inc., USA<\/td>
HFEABC

Presentation (link)<\/a>
Algorithm (link)<\/a><\/td>		4.54<\/td><\/tr>
6<\/td>
Edgar Morquecho (Electrical Engineer);
Santiago Torres (PhD);
Nelson Matute (Electrical Engineer);
Fabian Astudillo (PhD);
Julio Lopez (PhD)<\/td>
Faculty of Engineering, University of Cuenca<\/td>		DE-TLBO

Algorithm (link)<\/a><\/td>		3.97<\/td><\/tr>
7<\/td>
Dharmesh A. Dabhi, 
Kartik S. Pandya<\/td>
Charusat University, India<\/td>
EHL_PS_VNSO

Presentation (link)<\/a>
Algorithm (link)<\/a><\/td>		3.93<\/td><\/tr>
8
<\/strong>Best Algorithm in Testbed 2<\/span><\/td>
Kartik S. Pandya;
Dharmesh A. Dabhi <\/td>
Charusat University, India<\/td>
CE-CMAES


Presentation (link)<\/a>
Algorithm (link)<\/a><\/td>		3.92<\/td><\/tr>
9<\/td>
HaoYang Zhang<\/td>
China Southern Power Grid, China<\/td>
GASAPSO

Algorithm (link)<\/a><\/td>		3.90<\/td><\/tr>
10<\/td>
Ricardo Faia<\/td>
University of Salamanca, Spain<\/td>
PSO GBP

Algorithm (link)<\/a><\/td>		2.92<\/td><\/tr>
11<\/td>
Fabricio Loor<\/td>
National University of San Luis, Argentina<\/td>
AJSO

Algorithm (link)<\/a><\/td>		2.91<\/td><\/tr>
12<\/td>
Pedro Julian Garcia, 
Arturo Bretas, 
Sergio Rivera,<\/td>
UNAC, Colombia; University of Florida, USA<\/td>		VNS and DEEPSO

Algorithm (link)<\/a><\/td>		N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

UPDATES<\/h2>\n\n\n\n

COVID-19:<\/strong> Given the COVID-19 situation, both conferences have been converted into a fully exciting virtual conference. Conferences will still happen but virtually and the competition will continue as planned.<\/p>\n\n\n\n

IEEE CIS PRIZE: <\/strong>We are very glad that the IEEE Computational Intelligence Society (CIS) decided to sponsor this competition by providing a 500 $ prize to the participant that performs better in our rank. Stay tuned. Thanks, IEEE CIS!<\/p>\n\n\n\n

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Competition Outline<\/h2>\n\n\n\n

Following the success of the previous editions at WCCI 2018 (Rio de Janeiro, Brazil) and CEC\/GECCO 2019 (New Zealand and Prague, Czechia) we are launching a more challenging algorithm competition at major international conferences in the field of computational intelligence. This WCCI & GECCO 2020 competition proposes two testbeds in the energy domain:<\/p>\n\n\n\n

Testbed 1) optimization of a centralized day-ahead energy resource management problem in smart grids under environments with uncertainty. This testbed is similar to the past challenge using a challenging 500-scenario case study with a high degree of uncertainty. We also add some restrictions to the initialization of the initial solution and the allowed repairs and tweak-heuristics.<\/p>\n\n\n\n

Testbed 2) bi-level optimization of end-users\u2019 bidding strategies in local energy markets (LM). This test bed is constructed under the same framework of the past competitions (therefore, former competitors can adapt their algorithms to this new testbed), representing a complex bi-level problem in which competitive agents in the upper-level try to maximize their profits, modifying and depending on the price determined in the lower-level problem (i.e., the clearing price in the LM), thus resulting in a strong interdependence of their decisions.<\/p>\n\n\n\n

Check former competitions in http:\/\/www.gecad.isep.ipp.pt\/WCCI2018-SG-COMPETITION\/<\/a> and http:\/\/www.gecad.isep.ipp.pt\/ERM2019-Competition<\/a> <\/p>\n\n\n\n

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Competition goals <\/h2>\n\n\n\n

The WCCI & GECCO 2020 competition on \u201cEvolutionary Computation in the Energy Domain: Smart Grid Applications\u201d has the purpose of bringing together and testing state-ot-the-art Computational Intelligence (CI) techniques applied to energy domain problems, namely the energy resource management problem under uncertain environments and the optimal bidding of energy aggregators in local markets. The competition provides a coherent framework where participants and practitioners of CI can test their algorithms to solve two real-world optimization problems in the energy domain. The participants have the opportunity to evaluate if their algorithms can rank well in both problems since we understand the validity of the \u201cno-free lunch theorem\u201d, making this contest a unique opportunity worth to explore the applicability of the developed approaches in real-world problems beyond the typical benchmark and standardized CI problems.<\/p>\n\n\n\n

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Rules <\/h2>\n\n\n\n

-Participants will propose and implement metaheuristic algorithms (e.g., evolutionary algorithms, swarm intelligence, estimation of distribution algorithm, etc.) to solve two testbeds problems in the energy domain.-The organizers provide a framework, implemented in MATALAB\u00a9 2014b 64 bits (Download here)<\/a>, in which participants can easily test their algorithms (we also provide a differential evolution algorithm implementation as an example). The guidelines  (Download here)<\/a> include the necessary information to understand the problems, how the solutions are represented, and how the fitness function is evaluated. Those elements are common for all participants.<\/p>\n\n\n\n

-Since the proposed algorithms might have distinct sizes of population and run for a variable number of iterations, a maximum number of \u201c50000 function evaluations\u201d is allowed in each trial for all participants. The convergence properties of the algorithms are not a criterion to be qualified in this competition.<\/p>\n\n\n\n

– Only random seed initial solutions are allowed in this competition. Heuristics and special tweaks for initial solutions are not accepted.<\/p>\n\n\n\n

-20 independent trials should be performed in\nthe framework by each participant.
<\/p>\n\n\n\n

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How to submit an entry <\/h2>\n\n\n\n

-The winner will be the participant with the\nminimum ranking index in both testbeds, which is calculated as the average\nvalue over the 20 trials of the expected fitness value plus the standard\ndeviation. Considering the framework is the same for both testbeds, algorithms\nshould be able to run in both problems.<\/p>\n\n\n\n

–\nEach participant is kindly requested to put the text files corresponding to\nfinal results (see guideline document), as well as the implementation files\n(codes), obtained by using a specific optimizer, into a zipped folder named <\/p>\n\n\n\n

WCCI2020_testbedX_AlgorithmName<\/em>_ParticipantName<\/em>.zip (e.g. WCCI2020<\/em>_testbed2_DE<\/em>_Lezama<\/em>.zip).<\/p>\n\n\n\n

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Important Remarks <\/h2>\n\n\n\n

– Notice that submission of papers or assistance to CEC or GECCO by competition participants is not mandatory.<\/strong><\/p>\n\n\n\n

– You can submit a paper to the special session on Evolutionary Algorithms for Complex Optimization in the Energy Domain (CEC-26).<\/p>\n\n\n\n

Submit your results by May 31, 29:59 (GMT)<\/strong><\/p>\n\n\n\n

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Further related bibliography <\/h3>\n\n\n\n