{"id":755,"date":"2022-10-26T12:24:59","date_gmt":"2022-10-26T12:24:59","guid":{"rendered":"http:\/\/www.gecad.isep.ipp.pt\/ERM-competitions\/?page_id=755"},"modified":"2023-12-14T10:35:59","modified_gmt":"2023-12-14T10:35:59","slug":"2023-2","status":"publish","type":"page","link":"https:\/\/www.gecad.isep.ipp.pt\/ERM-competitions\/2023-2\/","title":{"rendered":"Call for Competition on Evolutionary Computation in the Energy Domain: Operation and Planning Applications"},"content":{"rendered":"\n

GECCO 2023<\/a> & IEEE CEC 2023<\/a> (Joint competition) <\/p>\n\n\n\n

1-5 July – Chicago, USA (CEC 2023) | 15-19 July – Lisbon, Portugal (GECCO 2023)<\/p>\n\n\n\n

Organized<\/strong> by ISEP and UNESP<\/p>\n\n\n\n

ISEP: <\/strong>Fernando Lezama, Jo\u00e3o Soares, Jos\u00e9 Almeida, Bruno Canizes, Zita Vale<\/p>\n\n\n\n

UNESP:<\/strong> Leonardo H. Macedo, Gabriel Puerta, Ruben Romero<\/p>\n\n\n\n

\n\n\n\n

Final Rank Results<\/h2>\n\n\n\n

Global Winners (normalized average of Track1 and Track2)<\/h2>\n\n\n\n
Final Rank<\/strong><\/td>Name<\/strong><\/td>Affiliation, Country<\/strong><\/td>Algorithm<\/strong><\/td>Rank Index<\/strong><\/td><\/tr>
1
IEEE CIS Prize \u2013 500 $<\/strong><\/td>		Ansel Y. Rodr\u00edguez Gonz\u00e1lez (1)(2), \u00c1ngel D\u00edaz Pacheco (4)(2), Ram\u00f3n Aranda (5)(2), Miguel \u00c1. \u00c1lvarez-Carmona (5)(2), Yoan Mart\u00ednez L\u00f3pez (3), Julio Madera (3)<\/td>(1) Unidad de Transferencia Tecnol\u00f3gica Tepic del Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, M\u00e9xico; (2) Consejo Nacional de Ciencia y Tecnolog\u00eda, M\u00e9xico; (3) Universidad de Camag\u00fcey, Cuba; (4) Universidad de Guanajuato; (5) Centro de Investigaci\u00f3n en Matem\u00e1ticas<\/td>Ring-Cellular Encode-Decode UMDA (RCEDUMDA)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		0.0164<\/td><\/tr>
2<\/td>Sergio Rivera (1), Sebastian Krumscheid (2), Kannappan Chettiar (3)<\/td>(1)  Universidad Nacional de Colombia (UN), Colombia; (2) Karlsruhe Institute of Technology (KIT), Germany; (3) SWITCHING BATTERY company<\/td>Mean-variance mapping optimization with Ring Cellular Encode-Decode UMDA and Hybrid-adaptive differential evolution (MVMO-RCEDUMDA-HyDE)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		0.0259<\/td><\/tr>
3<\/td>Haoxiang Qin (1), Wenlei Bai (2), Yi Xiang (1), Fangqing Liu (1), Yuyan Han (3), Ling Wang (4), and Kwang Y. Lee (5)  <\/td>(1) School of Software Engineering, South China University of Technology, Guangzhou 510006, China; (2) Oracle Energy and Water, Oracle America Inc., Austin, TX, USA; (3) School of Computer Science, Liaocheng University, Liaocheng 252059, China; (4) Department of Automation, Tsinghua University, Beijing 100084, China; (5) Department of Electrical and Computer Engineering, Baylor University, Waco, TX<\/td>Self-adaptive Collaborative Differential Evolutionary Algorithm (SADEA) 

Presentation<\/a>
Algorithm (link)<\/a><\/td>		0.0700<\/td><\/tr>
4<\/td>Dharmesh A. Dabhi (1), Kartik S. Pandya, Pratik Mochi (2), Rohit Salgrota (3)<\/td>(1) TECHNOLOGICAL UNIVERSITY DUBLIN,DUBLIN; (2) Department of Electrical Engineering, CSPIT,CHARUSAT UNIVERSITY,CHANGA, Gujarat, INDIA; (3) AGH UNIVERSITY OF SCIENCE & TECHNOLOGY, POLAND<\/td>CHAOTIC LEAVY HC2RCEDUMDA (CL_HC2RCEDUMDA)<\/td>0.1652<\/td><\/tr>
5<\/td>Edgar Morquecho (1), Santiago P. Torres (1), Jorge E. Idrovo (1), Mateo D. Llivisaca (1), Darwin F. Astudillo (1), Jose Jara-Alvear (2), Daniela Ballari (3), Ximena Gavela (4), Miguel A. Torres (5), Fabian Calero (6), Rommel Aguilar (6), Jhery Saavedra (6)<\/td>(1) Department of Electrical, Electronics, and Telecommunications Engineering (DEET), University of Cuenca, Cuenca, Ecuador; (2) CIENER Research Group, University of Azuay (UDA), Cuenca, Ecuador; (3) Instituto de Estudios de R\u00e9gimen Seccional del Ecuador\u201d Faculty of Science and Technology, University of Azuay, Cuenca; (4)Department of Electrical Engineering, Escuela Polit\u00e9cnica Nacional, Quito, Ecuador; (5) Faculty of Electrical and Computer Engineering (FIEC), Escuela Superior Polit\u00e9cnica del Litoral (ESPOL), Guayaquil,
Ecuador; (6) Corporaci\u00f3n El\u00e9ctrica del Ecuador (CELEC EP)<\/td>		Differential Evolution, Particle Swarm Optimization, Big Bang-Big Crunch (DE-PSO-BBBC)<\/td>0.2455<\/td><\/tr>
6<\/td>Yoan Mart\u00ednez-L\u00f3pez (1), Miguel Bethencourt (1), Julio Madera (1), Ansel Rodriguez (2), Wenlei Bai (3), Haoxiang Qin<\/td>(1) Camag\u00fcey University, Cuba; (2) CICESE, Mexico; (3) Oracle Energy and Water, Oracle America Inc., Austin, TX,USA; (4) School of Software Engineering, South China University of Technology, Guangzhou 510006, China<\/td>Vortex Island Estimation of Distribution Algorithm++ (VIEDA++)<\/td>0.2548<\/td><\/tr>
7<\/td>Dharmesh A. Dabhi (1), Kartik S. Pandya, Pratik Mochi (2), Rohit Salgrota (3)<\/td>(1) TECHNOLOGICAL UNIVERSITY DUBLIN, DUBLIN; (2) Department of Electrical Engineering, CSPIT, CHARUSAT UNIVERSITY, CHANGA, Gujarat, INDIA; (3) AGH UNIVERSITY OF SCIENCE & TECHNOLOGY, POLAND<\/td>CHAOTIC CAUCHY HC2RCEDUMDA (CC_HC2RCEDUMDA)<\/td>0.5114<\/td><\/tr>
8<\/td>Yoan Mart\u00ednez-L\u00f3pez (1), Miguel Bethencourt (1), Julio Madera (1), Ansel Rodriguez (2), Wenlei Bai (3), Haoxiang Qin<\/td>(1) Camag\u00fcey University, Cuba; (2) CICESE, Mexico; (3) Oracle Energy and Water, Oracle America Inc., Austin, TX,USA; (4) School of Software Engineering, South China University of Technology, Guangzhou 510006, China<\/td>Cellular UMDA using Gamma distribution++ (CUMDANGamma++)<\/td>0.5411<\/td><\/tr>
9<\/td>Br\u00edgida Teixeira (1), Ricardo Faia (1), Tiago Pinto (2)<\/td>(1) University of Salamanca; (2) University Tr\u00e1s-os-Montes and Alto Douro<\/td>Modified Wild Horse Optimizer (Mod-WHO)<\/td>0.8437<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Track 1<\/h3>\n\n\n\n
Final Rank<\/strong><\/td>Name<\/strong><\/td>Affiliation, Country<\/strong><\/td>Algorithm<\/strong><\/td>Rank Index<\/strong><\/td><\/tr>
1<\/strong><\/td>Ansel Y. Rodr\u00edguez Gonz\u00e1lez (1)(2), \u00c1ngel D\u00edaz Pacheco (4)(2), Ram\u00f3n Aranda (5)(2), Miguel \u00c1. \u00c1lvarez-Carmona (5)(2), Yoan Mart\u00ednez L\u00f3pez (3),  Julio Madera (3)<\/td>(1) Unidad de Transferencia Tecnol\u00f3gica Tepic del Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, M\u00e9xico; (2) Consejo Nacional de Ciencia y Tecnolog\u00eda, M\u00e9xico; (3) Universidad de Camag\u00fcey, Cuba; (4) Universidad de Guanajuato; (5) Centro de Investigaci\u00f3n en Matem\u00e1ticas<\/td>Ring-Cellular Encode-Decode UMDA (RCEDUMDA)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		35158.01<\/td><\/tr>
2<\/td>Dharmesh A. Dabhi (1), Kartik S. Pandya, Pratik Mochi (2), Rohit Salgrota (3)<\/td>(1) TECHNOLOGICAL UNIVERSITY DUBLIN,DUBLIN; (2) Department of Electrical Engineering, CSPIT,CHARUSAT UNIVERSITY,CHANGA, Gujarat, INDIA; (3) AGH UNIVERSITY OF SCIENCE & TECHNOLOGY, POLAND<\/td>CHAOTIC CAUCHY HC2RCEDUMDA (CC_HC2RCEDUMDA)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		36421.89<\/td><\/tr>
3<\/td>Sergio Rivera (1), Sebastian Krumscheid (2), Kannappan Chettiar (3)<\/td>(1)  Universidad Nacional de Colombia (UN), Colombia; (2) Karlsruhe Institute of Technology (KIT), Germany; (3) SWITCHING BATTERY company<\/td>Mean-variance mapping optimization and Ring Cellular Encode-Decode UMDA (MVMO-RCEDUMDA)<\/td>37743.50<\/td><\/tr>
3<\/td>Sergio Rivera (1), Sebastian Krumscheid (2), Kannappan Chettiar (3)<\/td>(1)  Universidad Nacional de Colombia (UN), Colombia; (2) Karlsruhe Institute of Technology (KIT), Germany; (3) SWITCHING BATTERY company<\/td>Mean-variance mapping optimization with Ring Cellular Encode-Decode UMDA and Hybrid-adaptive differential evolution (MVMO-RCEDUMDA-HyDE)

Presentati<\/a>on<\/a>
Algorithm (link)<\/a><\/td>		37799.28<\/td><\/tr>
4<\/td>Yoan Mart\u00ednez-L\u00f3pez (1), Miguel Bethencourt (1), Julio Madera (1), Ansel Rodriguez (2), Wenlei Bai (3), Haoxiang Qin<\/td>(1) Camag\u00fcey University, Cuba; (2) CICESE, Mexico; (3) Oracle Energy and Water, Oracle America Inc., Austin, TX,USA; (4) School of Software Engineering, South China University of Technology, Guangzhou 510006, China<\/td>Cellular UMDA using Gamma distribution++ (CUMDANGamma++)<\/td>39702.14<\/td><\/tr>
5<\/td>Dharmesh A. Dabhi (1), Kartik S. Pandya, Pratik Mochi (2), Rohit Salgrota (3)<\/td>(1) TECHNOLOGICAL UNIVERSITY DUBLIN,DUBLIN; (2) Department of Electrical Engineering, CSPIT,CHARUSAT UNIVERSITY,CHANGA, Gujarat, INDIA; (3) AGH UNIVERSITY OF SCIENCE & TECHNOLOGY, POLAND<\/td>CHAOTIC LEAVY HC2RCEDUMDA (CL_HC2RCEDUMDA)<\/td>40255.73<\/td><\/tr>
6<\/td>Vasundhara Mahaja, Maxwell Mendonca, Deepty George<\/td>Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat – 395007, India<\/td>Chaotic Levy Hill-Climbing to Ring-Cellular Encode-Decode UMDA (CL-HC2RCEDUMDA)<\/td>41095.59<\/td><\/tr>
7<\/td>Haoxiang Qin (1), Wenlei Bai (2), Yi Xiang (1), Fangqing Liu (1), Yuyan Han (3), Ling Wang (4), and Kwang Y. Lee (5)  <\/td>(1) School of Software Engineering, South China University of Technology, Guangzhou 510006, China; (2) Oracle Energy and Water, Oracle America Inc., Austin, TX, USA; (3) School of Computer Science, Liaocheng University, Liaocheng 252059, China; (4) Department of Automation, Tsinghua University, Beijing 100084, China; (5) Department of Electrical and Computer Engineering, Baylor University, Waco, TX<\/td>Self-adaptive Collaborative Differential Evolutionary Algorithm (SADEA) <\/td>41551.98<\/td><\/tr>
8<\/td>Maoxin He (1), Chixin Xiao (2), Dechen Jiang (2)<\/td>(1) School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411100, China; (2) School of Computer Science & School of Cyberspace Science, Xiangtan University, Xiangtan 411100, China<\/td>Hybrid of Surrogate and Differential Evolution(HXJ)<\/td>48311.69<\/td><\/tr>
9<\/td>Dechen Jiang (1), Chixin Xiao (1), Maoxin He (2)<\/td>(1) School of Computer Science & School of Cyberspace Science, Xiangtan University, Xiangtan 411100, China;(2)  School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411100, China<\/td>Surrogate Model Evolution(JXH)<\/td>48739.90<\/td><\/tr>
10<\/td>Edgar Morquecho (1), Santiago P. Torres (1), Jorge E. Idrovo (1), Mateo D. Llivisaca (1), Darwin F. Astudillo (1), Jose Jara-Alvear (2), Daniela Ballari (3), Ximena Gavela (4), Miguel A. Torres (5), Fabian Calero (6), Rommel Aguilar (6), Jhery Saavedra (6)<\/td>(1) Department of Electrical, Electronics, and Telecommunications Engineering (DEET), University of Cuenca, Cuenca, Ecuador; (2) CIENER Research Group, University of Azuay (UDA), Cuenca, Ecuador; (3) Instituto de Estudios de R\u00e9gimen Seccional del Ecuador\u201d Faculty of Science and Technology, University of Azuay, Cuenca; (4)Department of Electrical Engineering, Escuela Polit\u00e9cnica Nacional, Quito, Ecuador; (5) Faculty of Electrical and Computer Engineering (FIEC), Escuela Superior Polit\u00e9cnica del Litoral (ESPOL), Guayaquil,
Ecuador; (6) Corporaci\u00f3n El\u00e9ctrica del Ecuador (CELEC EP)<\/td>		Differential Evolution, Particle Swarm Optimization, Big Bang-Big Crunch (DE-PSO-BBBC)<\/td>62286.12<\/td><\/tr>
11<\/td>Yoan Mart\u00ednez-L\u00f3pez (1), Miguel Bethencourt (1), Julio Madera (1), Ansel Rodriguez (2), Wenlei Bai (3), Haoxiang Qin<\/td>(1) Camag\u00fcey University, Cuba; (2) CICESE, Mexico; (3) Oracle Energy and Water, Oracle America Inc., Austin, TX,USA; (4) School of Software Engineering, South China University of Technology, Guangzhou 510006, China<\/td>Vortex Island Estimation of Distribution Algorithm++ (VIEDA++)<\/td>62805.96<\/td><\/tr>
12<\/td>Br\u00edgida Teixeira (1), Ricardo Faia (1), Tiago Pinto (2)<\/td>(1) University of Salamanca; (2) University Tr\u00e1s-os-Montes and Alto Douro<\/td>Modified Wild Horse Optimizer (Mod-WHO)<\/td>79772.86<\/td><\/tr>
13<\/td>Vasundhara Mahaja, Maxwell Mendonca, Deepty George<\/td>Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat – 395007, India<\/td>Genetic Algorithm and Particle Swarm Optimization (GA-PSO)<\/td>86914.97<\/td><\/tr>
13<\/td>Vasundhara Mahaja, Maxwell Mendonca, Deepty George<\/td>Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat – 395007, India<\/td>Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization (GA-SA-PSO)<\/td>89122.45<\/td><\/tr>
13<\/td>Vasundhara Mahaja, Maxwell Mendonca, Deepty George<\/td>Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat – 395007, India<\/td>Differential Evolution with Teaching Learning based Optimization (DE-TLBO)<\/td>90410.50<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Track 2<\/h3>\n\n\n\n
Final Rank<\/strong><\/td>Name<\/strong><\/td>Affiliation, Country<\/strong><\/td>Algorithm<\/strong><\/td>Rank Index<\/strong><\/td><\/tr>
1<\/strong><\/td>Edgar Morquecho (1), Santiago P. Torres (1), Jorge E. Idrovo (1), Mateo D. Llivisaca (1), Darwin F. Astudillo (1), Jose Jara-Alvear (2), Daniela Ballari (3), Ximena Gavela (4), Miguel A. Torres (5), Fabian Calero (6), Rommel Aguilar (6), Jhery Saavedra (6)<\/td>(1) Department of Electrical, Electronics, and Telecommunications Engineering (DEET), University of Cuenca, Cuenca, Ecuador; (2) CIENER Research Group, University of Azuay (UDA), Cuenca, Ecuador; (3) Instituto de Estudios de R\u00e9gimen Seccional del Ecuador\u201d Faculty of Science and Technology, University of Azuay, Cuenca; (4)Department of Electrical Engineering, Escuela Polit\u00e9cnica Nacional, Quito, Ecuador; (5) Faculty of Electrical and Computer Engineering (FIEC), Escuela Superior Polit\u00e9cnica del Litoral (ESPOL), Guayaquil,
Ecuador; (6) Corporaci\u00f3n El\u00e9ctrica del Ecuador (CELEC EP)<\/td>		Differential Evolution, Particle Swarm Optimization, Big Bang-Big Crunch (DE-PSO-BBBC)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		4481637.30<\/td><\/tr>
2<\/td>Sergio Rivera (1), Sebastian Krumscheid (2), Kannappan Chettiar (3)<\/td>(1)  Universidad Nacional de Colombia (UN), Colombia; (2) Karlsruhe Institute of Technology (KIT), Germany; (3) SWITCHING BATTERY company<\/td>Mean-variance mapping optimization and Hybrid-adaptive differential evolution (MVMO-HyDE)
<\/td>		4799485.40<\/td><\/tr>
2<\/td>Sergio Rivera (1), Sebastian Krumscheid (2), Kannappan Chettiar (3)<\/td>(1)  Universidad Nacional de Colombia (UN), Colombia; (2) Karlsruhe Institute of Technology (KIT), Germany; (3) SWITCHING BATTERY company<\/td>Mean-variance mapping optimization with Ring Cellular Encode-Decode UMDA and Hybrid-adaptive differential evolution (MVMO-RCEDUMDA-HyDE)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		4869145.80<\/td><\/tr>
3<\/td>Yoan Mart\u00ednez-L\u00f3pez (1), Miguel Bethencourt (1), Julio Madera (1), Ansel Rodriguez (2), Wenlei Bai (3), Haoxiang Qin<\/td>(1) Camag\u00fcey University, Cuba; (2) CICESE, Mexico; (3) Oracle Energy and Water, Oracle America Inc., Austin, TX,USA; (4) School of Software Engineering, South China University of Technology, Guangzhou 510006, China<\/td>Vortex Island Estimation of Distribution Algorithm++ (VIEDA++)

Presentation<\/a>
Algorithm (link)<\/a><\/td>		4925701.35<\/td><\/tr>
4<\/td>Haoxiang Qin (1), Wenlei Bai (2), Yi Xiang (1), Fangqing Liu (1), Yuyan Han (3), Ling Wang (4), and Kwang Y. Lee (5)  <\/td>(1) School of Software Engineering, South China University of Technology, Guangzhou 510006, China; (2) Oracle Energy and Water, Oracle America Inc., Austin, TX, USA; (3) School of Computer Science, Liaocheng University, Liaocheng 252059, China; (4) Department of Automation, Tsinghua University, Beijing 100084, China; (5) Department of Electrical and Computer Engineering, Baylor University, Waco, TX<\/td>Self-adaptive Collaborative Differential Evolutionary Algorithm (SADEA) <\/td>5091978.70<\/td><\/tr>
5<\/td>Ansel Y. Rodr\u00edguez Gonz\u00e1lez (1)(2), \u00c1ngel D\u00edaz Pacheco (4)(2), Ram\u00f3n Aranda (5)(2), Miguel \u00c1. \u00c1lvarez-Carmona (5)(2), Yoan Mart\u00ednez L\u00f3pez (3),  Julio Madera (3)<\/td>(1) Unidad de Transferencia Tecnol\u00f3gica Tepic del Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, M\u00e9xico; (2) Consejo Nacional de Ciencia y Tecnolog\u00eda, M\u00e9xico; (3) Universidad de Camag\u00fcey, Cuba; (4) Universidad de Guanajuato; (5) Centro de Investigaci\u00f3n en Matem\u00e1ticas<\/td>Ring-Cellular Encode-Decode UMDA (RCEDUMDA)<\/td>5186982.80<\/td><\/tr>
6<\/td>Dharmesh A. Dabhi (1), Kartik S. Pandya, Pratik Mochi (2), Rohit Salgrota (3)<\/td>(1) TECHNOLOGICAL UNIVERSITY DUBLIN,DUBLIN; (2) Department of Electrical Engineering, CSPIT,CHARUSAT UNIVERSITY,CHANGA, Gujarat, INDIA; (3) AGH UNIVERSITY OF SCIENCE & TECHNOLOGY, POLAND<\/td>CHAOTIC LEAVY HC2RCEDUMDA (CL_HC2RCEDUMDA)<\/td>7460548.45<\/td><\/tr>
7<\/td>Br\u00edgida Teixeira (1), Ricardo Faia (1), Tiago Pinto (2)<\/td>(1) University of Salamanca; (2) University Tr\u00e1s-os-Montes and Alto Douro<\/td>Modified Wild Horse Optimizer (Mod-WHO)<\/td>14566299.20<\/td><\/tr>
8<\/td>Yoan Mart\u00ednez-L\u00f3pez (1), Miguel Bethencourt (1), Julio Madera (1), Ansel Rodriguez (2), Wenlei Bai (3), Haoxiang Qin<\/td>(1) Camag\u00fcey University, Cuba; (2) CICESE, Mexico; (3) Oracle Energy and Water, Oracle America Inc., Austin, TX,USA; (4) School of Software Engineering, South China University of Technology, Guangzhou 510006, China<\/td>Cellular UMDA using Gamma distribution++ (CUMDANGamma++)<\/td>16610481.38<\/td><\/tr>
9<\/td>Dharmesh A. Dabhi (1), Kartik S. Pandya, Pratik Mochi (2), Rohit Salgrota (3)<\/td>(1) TECHNOLOGICAL UNIVERSITY DUBLIN,DUBLIN; (2) Department of Electrical Engineering, CSPIT,CHARUSAT UNIVERSITY,CHANGA, Gujarat, INDIA; (3) AGH UNIVERSITY OF SCIENCE & TECHNOLOGY, POLAND<\/td>CHAOTIC CAUCHY HC2RCEDUMDA (CC_HC2RCEDUMDA)<\/td>12090611158<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

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

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 GECCO 2023 competition proposes two tracks in the energy domain:<\/p>\n\n\n\n

Track 1) Risk-based optimization of aggregators\u2019 day-ahead energy resource management (ERM) considering the uncertainty of high penetration of distributed energy resources (DER). This testbed represents a centralized day-ahead ERM in a smart grid with a 13-bus distribution network using a 15-scenario case study with 3 scenarios considering extreme events (high impact and low probability). A conditional value-at-risk (CVaR) mechanism is used to measure the risk associated with extreme events for a confidence level (\u03b1) of 95%. We also add some restrictions to the initialization of solutions and allowed repairs and tweak-heuristics.<\/p>\n\n\n\n

Track 2) Transmission Network Expansion Planning: Long-term transmission network expansion planning (TNEP) is a classic problem in power systems. The objective is to find the optimal expansion plan that identifies the transmission lines that must be installed in the system to allow a proper operation within a predefined planning horizon with the lowest investment cost. The optimal expansion plan should define where and how many lines should be installed. A nonconvex mixed-integer nonlinear programming formulation is used to model the problem. The Northeast Brazilian transmission system is considered a case study. Note: Both tracks are developed under the same framework as past competitions.<\/p>\n\n\n\n

\n\n\n\n

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

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<\/a>.<\/p>\n\n\n\n

Using the same framework, we have implemented several benchmark problems over the years. This year, we are keeping the 2022 track related to energy resource management considering risk measurement tools (a more recent problem in the energy domain) and also including a second track related to the planning of transmission systems.<\/p>\n\n\n\n

\n\n\n\n

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

– Participants will propose and implement metaheuristic algorithms (e.g., evolutionary algorithms, swarm intelligence, estimation of distribution algorithms, etc.) to solve any of the two-track problems in the energy domain. It will be considered independent entries for each track, i.e., two independent tracks.<\/p>\n\n\n\n

– The organizers provide a framework (Download codes<\/a>), implemented in MATALAB\u00a9 2018a 64 bits, in which participants can easily test their algorithms (we also provide a differential evolution algorithm implementation as an example). The guidelines (Download<\/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

– Because the proposed algorithms may have different population sizes and run for a variable number of iterations, each trial allows a maximum number of “function evaluations”, namely 5000 for track 1 and 20,000 for track 2. The convergence properties of the algorithms are not a qualification criterion for this competition.<\/p>\n\n\n\n

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

\n\n\n\n

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

– The winner will be the participant with the minimum ranking index in each independent track, calculated as the average value over the 20 trials of the expected fitness value plus the standard deviation. We will make an independent rank for each track (i.e., we will have a winner for each track).<\/p>\n\n\n\n

– Each participant is kindly requested to put the text files corresponding to the final results (see guideline document), as well as the implementation files (codes), obtained by using a specific optimizer, into a zipped folder named CEC2023_trackX_AlgorithmName_ParticipantName.zip (e.g., CECI2023_track2_DE_Lezama.zip).<\/p>\n\n\n\n

The zipped folder must be summited to jan@isep.ipp.pt; flz@isep.ipp.pt, jorga@isep.ipp.pt
by 30 June 2023 (anywhere on Earth)<\/strong><\/em><\/p>\n\n\n\n

\n\n\n\n

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

– Notice that submission of papers or assistance to CEC and GECCO by competition participants is not mandatory.<\/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). Submit it here<\/a> – select the SS name “Evolutionary Algorithms for Complex Optimization in the Energy Domain” as the primary subject area.<\/p>\n\n\n\n

– 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<\/strong> \u2014 The submission deadline is April 2023<\/strong>. Submit it here<\/a> (Competition Entry Submissions<\/strong>)<\/p>\n\n\n\n

IEEE CIS PRIZE:<\/strong><\/p>\n\n\n\n

We are glad to announce that our competition will offer an IEEE Computational Intelligence Society (CIS) prize of 500 $<\/strong> for the participant with the best normalized average rank of the two tracks<\/strong>. Also, an honorable mention will be given to the winner of each track. Good luck, and stay tuned. Thanks!<\/p>\n\n\n\n

Submit your results by June 1<\/s> June 30th (extended)<\/strong> 2023<\/strong> (anywhere on earth)<\/p>\n\n\n\n

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