A review on the coral reefs optimization algorithm: new development lines and current applications

The simulation of biological processes has produced some of the most important meta-heuristics algorithms for optimization. Evolutionary algorithms were the first, and probably the most applied, algorithms coming from biological inspiration, but there have been many more, specially in the last few years. This paper describes a special class of evolutionary algorithms recently proposed, the coral reefs optimization algorithm (CRO), which simulates some specific biological processes that occur in real coral reefs. The simulation of these processes leads to an evolutionary algorithm in which similarities with Simulated Annealing have been introduced. Moreover, the inclusion of alternative processes occurring in coral reefs produces very effective co-evolution versions of the CRO algorithm, specially well suited for optimization problems with inherent variable length encodings, or able to co-evolve several exploration patterns within the same population. All these issues related to the CRO approach are thoroughly described in the paper, and also a fully description of the main applications of the algorithm in engineering optimization problems is given to close this first review on the CRO.

[1]  Bilal Alatas,et al.  ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization , 2011, Expert Syst. Appl..

[2]  D. Lirman,et al.  Fragmentation in the branching coral Acropora palmata (Lamarck): growth, survivorship, and reproduction of colonies and fragments. , 2000, Journal of experimental marine biology and ecology.

[3]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[4]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[5]  Petros Koumoutsakos,et al.  Optimization based on bacterial chemotaxis , 2002, IEEE Trans. Evol. Comput..

[6]  Dennis Weyland,et al.  A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a "Novel" Methodology , 2010, Int. J. Appl. Metaheuristic Comput..

[7]  J. A. Portilla-Figueras,et al.  The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems , 2014, TheScientificWorldJournal.

[8]  Christian Esposito,et al.  A coral-reefs and Game Theory-based approach for optimizing elastic cloud resource allocation , 2018, Future Gener. Comput. Syst..

[9]  Sancho Salcedo-Sanz,et al.  Effective multi-objective optimization with the coral reefs optimization algorithm , 2016 .

[10]  Ali Kaveh,et al.  Colliding bodies optimization: A novel meta-heuristic method , 2014 .

[11]  Sancho Salcedo-Sanz,et al.  One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms , 2015 .

[12]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[13]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[14]  Emanuel Falkenauer,et al.  The Grouping Genetic Algorithm , 1996 .

[15]  Jeffrey O. Kephart,et al.  A biologically inspired immune system for computers , 1994 .

[16]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[17]  Jody M. Webster,et al.  Coral reefs and sea-level change , 2014 .

[18]  Sancho Salcedo-Sanz,et al.  A Novel Coral Reefs Optimization Algorithm for Multi-objective Problems , 2013, IDEAL.

[19]  Zhiyong Yang,et al.  A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training , 2016, Cognitive Neurodynamics.

[20]  N. E. Chadwick.  INTERSPECIFIC AGGRESSIVE BEHAVIOR OF THE CORALLIMORPHARIAN CORYNACTIS CALIFORNICA (CNIDARIA: ANTHOZOA): EFFECTS ON SYMPATRIC CORALS AND SEA ANEMONES. , 1987, The Biological bulletin.

[21]  Anne M. P. Canuto,et al.  Cluster Ensembles Optimization Using Coral Reefs Optimization Algorithm , 2016, ICANN.

[22]  Dennis Weyland,et al.  A critical analysis of the harmony search algorithm—How not to solve sudoku , 2015 .

[23]  Sancho Salcedo-Sanz,et al.  Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach , 2014 .

[24]  Juan Julián Merelo Guervós,et al.  Determining the significance and relative importance of parameters of a simulated quenching algorithm using statistical tools , 2011, Applied Intelligence.

[25]  Michael P. Lesser,et al.  Experimental biology of coral reef ecosystems , 2004 .

[26]  John W M Bush,et al.  The fine art of surfacing: its efficacy in broadcast spawning. , 2012, Journal of theoretical biology.

[27]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[28]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[29]  A. Genin,et al.  Effects of flow on competitive superiority in scleractinian corals , 1994 .

[30]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[31]  Francisco Herrera,et al.  A coral reefs optimization algorithm with substrate layers and local search for large scale global optimization , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[32]  Sancho Salcedo-Sanz,et al.  A coral-reef optimization algorithm for the optimal service distribution problem in mobile radio access networks , 2014, Trans. Emerg. Telecommun. Technol..

[33]  Zong Woo Geem,et al.  A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction , 2015 .

[34]  Javier Del Ser,et al.  A novel Coral Reefs Optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids , 2016, Soft Comput..

[35]  Javier Del Ser,et al.  A Coral Reefs Optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion , 2014, Appl. Soft Comput..

[36]  M. Vermeij.  Substrate composition and adult distribution determine recruitment patterns in a Caribbean brooding coral , 2005 .

[37]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[38]  Javier Del Ser,et al.  A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteria , 2016, Expert Syst. Appl..

[39]  Yujun Zheng.  Water wave optimization: A new nature-inspired metaheuristic , 2015, Comput. Oper. Res..

[40]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[41]  Tantikorn Pichpibul,et al.  An improved Clarke and Wright savings algorithm for the capacitated vehicle routing problem , 2012 .

[42]  J. E. Smith,et al.  Survival and settlement success of coral planulae: independent and synergistic effects of macroalgae and microbes , 2009, Oecologia.

[43]  Jack J. Middelburg,et al.  Surviving in a Marine Desert: The Sponge Loop Retains Resources Within Coral Reefs , 2013, Science.

[44]  Joong Hoon Kim,et al.  Harmony Search Algorithm: A Unique Music-inspired Algorithm☆ , 2016 .

[45]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[46]  Daniel A. Brazeau,et al.  Self-fertilization in brooding hermaphroditic Caribbean corals: Evidence from molecular markers , 1998 .

[47]  Sancho Salcedo-Sanz,et al.  Offshore wind farm design with the Coral Reefs Optimization algorithm , 2014 .

[48]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[49]  Harun Kemal Ozturk,et al.  Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach , 2004 .

[50]  Kenneth Sörensen,et al.  Metaheuristics - the metaphor exposed , 2015, Int. Trans. Oper. Res..

[51]  Nancy Knowlton,et al.  Corals and Coral Reefs , 2013 .

[52]  Javier Serrano González,et al.  A review and recent developments in the optimal wind-turbine micro-siting problem , 2014 .

[53]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[54]  A. Kaveh,et al.  A new meta-heuristic method: Ray Optimization , 2012 .

[55]  Turan Paksoy,et al.  A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey , 2012 .

[56]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[57]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[58]  Chunyan Miao,et al.  A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks , 2015, Sensors.

[59]  M. J. Mahjoob,et al.  A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search , 2010, Comput. Math. Appl..

[60]  Anne M. P. Canuto,et al.  Applying the Coral Reefs Optimization algorithm to clustering problems , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[61]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[62]  J. Ser,et al.  The Coral Reefs Optimization Algorithm : An Efficient Meta-heuristic for Solving Hard Optimization Problems , 2013 .

[63]  Vivek Patel,et al.  An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems , 2012 .

[64]  Pablo Cortés,et al.  Viral systems: A new bio-inspired optimisation approach , 2008, Comput. Oper. Res..

[65]  Hans-Paul Schwefel,et al.  Evolution strategies – A comprehensive introduction , 2002, Natural Computing.

[66]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[67]  Sancho Salcedo-Sanz,et al.  Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach , 2014 .

[68]  Hideyuki Yamashiro,et al.  Experimental study of growth and asexual reproduction in Diaseris distorta (Michelin, 1843), a free-living fungiid coral , 1998 .

[69]  Shu-Cherng Fang,et al.  An Electromagnetism-like Mechanism for Global Optimization , 2003, J. Glob. Optim..

[70]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[71]  C. Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[72]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[73]  Yong Deng,et al.  A Bio-Inspired Method for the Constrained Shortest Path Problem , 2014, TheScientificWorldJournal.

[74]  P. Bassett-Smith.  Coral Reefs , 1889, Nature.

[75]  Peter A. Todd,et al.  Vertical distribution and settlement competencies in broadcast spawning coral larvae: Implications for dispersal models , 2011 .

[76]  Sancho Salcedo-Sanz,et al.  New coral reefs-based approaches for the model type selection problem: a novel method to predict a nation's future energy demand , 2017, Int. J. Bio Inspired Comput..