CEC2016 Special Session

Special Session & Competitions on Real-Parameter Single Objective Optimization (4 Different Cases)

at CEC-2016, Vancouver, Canada, 25-29 July 2016

Organizers: P N Suganthan, Mostafa Z Ali, Qin Chen J J Liang, and B Y Qu.

https://github.com/P-N-Suganthan

If you face any difficulties, please inform me ( epnsugan@ntu.edu.sg ).

1. Q. Chen, B. Liu, Q. Zhang, J. J. Liang, P. N. Suganthan, B. Y. Qu, "Problem Definition and Evaluation Criteria for CEC 2015 Special Session and Competition on Bound Constrained Single-Objective Computationally Expensive Numerical Optimization", Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore, Nov 2014. (Computationally expensive case)

2. J. J. Liang, B. Y. Qu, P. N. Suganthan, Q. Chen, "Problem Definitions and Evaluation Criteria for the CEC 2015 Competition on Learning-based Real-Parameter Single Objective Optimization", Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore, Nov 2014. (Learning-based case)

3. B. Y. Qu, J. J. Liang, Z. Y. Wang, Q. Chen, P. N. Suganthan, "Novel Benchmark Functions for Continuous Multimodal Optimization with Comparative Results," Swarm and Evolutionary Computation, doi:10.1016/j.swevo.2015.07.003. (Final Solution Submission Format for a conference paper is Given Here) (Multi-solution niching case)

4. J. J. Liang, B-Y. Qu, P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization", Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore, December 2013. (Single parameter-operator set based case)

This round, we are making use of the older problems developed for CEC 2015 and CEC 2014. The distinction between #2 and #4 is that #2 allows you to tune parameters separately for each problem while #4 requires you to use the same parameter setting for all problems. Authors are asked to participate in each of the above separately, i.e. a single paper is expected to tackle only one class of problems above.

Software and Data: https://github.com/P-N-Suganthan

Comparative Analysis Slides: https://github.com/P-N-Suganthan

Papers Using CEC 2014 Benchmarks (Single Parameter and Operator Set)

UMOEAII

Testing United Multi operator Evolutionary AlgorithmsII on Single Objective Optimization Problems, Saber Elsayed, Noha Hamza and Ruhul Sarker (Joint-Winner)

LSHADE_EpSin

An Ensemble Sinusoidal Parameter Adaptation incorporated with L-SHADE for solving CEC 2014 problems, Noor Awad, Mostafa Ali, Ponnuthurai Suganthan and Robert Reynolds (Joint-Winner)

MC-SHADE

Success History Based Adaptive Differential Evolution Algorithm with Multi Chaotic Framework for Parent Selection Performance, Adam Viktorin, Michal Pluhacek and Roman Senkerik

iLSHADE

Improved LSHADE Algorithm for Single Objective Real Parameter Optimization, Janez Brest, Mirjam Sepesy Maucec and Borko Boskovic

SSEABC

Self adaptive Search Equation based Artificial Bee Colony Algorithm on the CEC 2014 Benchmark Functions, Gurcan Yavuz, Dogan Aydin and Thomas Stuetzle

SPMGTLO

Single Phase Multi-Group Teaching Learning Algorithm, Remya Kommadath, Sivadurgaprasad Chinta and Prakash Kotecha

AEPDJADE

Differential Evolution with Auto enhanced Population Diversity: the Experiments on the CEC'2016 Competition, Ming Yang, Jing Guan and Li Changhe

SHADE4

Evaluating the Performance of SHADE with Competing Strategies on CEC 2014 Single Parameter Test Suite, Petr Bujok, Josef Tvrdik and Radka Polakova

LSHADE44

Evaluating the Performance of LSHADE with Competing Strategies on CEC2014 Single Parameteroperator Test Suite, Radka Polakova, Josef Tvrdik and Petr Bujok

Papers Using CEC 2015 Benchmarks (Learning-Based, Tunable for each Problem)

MVMO

Solving the CEC2016 Real-Parameter Single Objective Optimization Problems through MVMO-PHM (Technical Report), José L. Rueda, István Erlich (Winner)

CCLSHADE

Cooperative Co-evolution using LSHADE with Restarts For The CEC15 Benchmarks, Mohammed ElAbd

LSHADE44

LSHADE with Competing Strategies Applied to CEC2015 Learning based Test Suite, Radka Polakova, Josef Tvrdik and Petr Bujok

AsAMP–dD

An Asynchronous Adaptive Multi population Model for Distributed Differential Evolution, Ivanoe De Falco, Antonio Della Cioppa, Umberto Scafuri and Ernesto Tarantino

SOMA

Competition On Learning-based Real-Parameter Single Objective Optimization by SOMA Swarm Based Algorithm with SOMARemove Strategy, Ivan Zelinka and Lukas Tomaszek

Papers Using CEC 2015 Expensive Benchmarks

MVMO

Solving the CEC2016 Real-Parameter Single Objective Optimization Problems through MVMO-PHM (Technical Report), José L. Rueda, István Erlich (Winner)

SPMGTLO

Single Phase MultiGroup Teaching Learning Algorithm for Computationally Expensive Numerical Optimization, Remya Kommadath, Sivadurgaprasad Chinta and Prakash Kotecha

AsBeC_tuned

A hybrid ABC for expensive optimizations: CEC 2016 competition benchmark, Enrico Ampellio and Luca Vassio

SHTS

Simultaneous Heat Transfer Search for Computationally Expensive Numerical Optimization, Debasis Maharana and Prakash Kotecha

RYYPO

Reduced YinYang Pair Optimization and its Performance on the CEC 2016 Expensive Case, Varun Punnathanam and Prakash Kotecha

BaMSOO

Analysis of the Bayesian Multi-Scale Optimistic Optimization on the CEC2016 and BBOB Testbeds, Abdullah AlDujaili and Suresh Sundaram

Paper Submission

Authors must strictly follow the manuscript preparation instructions: http://www.wcci2016.org/submission.php

When submitting, please make sure that you select "CEC42: Special Session Associated with Competition on Bound Constrained Single Objective Numerical Optimization" as the "Main Research Topic" for all papers making use of these benchmarks.

Paper Submission Deadline

We will follow the extended deadlines as determined by WCCI 2016.

Request for Feedback

If you have suggestions to improve the technical report or if you find any potential bug in the codes, please inform us ( epnsugan@ntu.edu.sg ).