Chemical Reaction Optimization
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We developed a chemical reaction-inspired optimization algorithm, Chemical Reaction Optimization (CRO). It was inspired by chemical reaction, a natural phenomenon in which reactants, in terms of molecules, collide with each other, pass through certain intermediate states, and finally stabilize as products with minimal energy. CRO is a successful general-purpose optimization algorithm by applying it to problems in many different domains, including operations research, communications and networking, computing, mathematics, finance, computational intelligence, bioinformatics, and energy and environment. CRO has opened the door for an emerging family of evolutionary computation and optimization. We are interested in CRO applications in different domains, its theoretic properties, hybridation with other algorithms, and further development in multi-objective optimization and multi-level optimization.
References
Albert Y.S. Lam and Victor O.K. Li, “Chemical-Reaction-Inspired Metaheuristic for Optimization,” IEEE Transactions on Evolutionary Computation, vol. 14, no. 3, pp. 381–399, June 2010.
Jin Xu, Albert Y.S. Lam, and Victor O.K. Li, “Chemical Reaction Optimization for Task Scheduling in Grid Computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 22, no. 10, pp. 1624–1631, Oct. 2011.
Albert Y.S. Lam and Victor O.K. Li, “Chemical Reaction Optimization: A Tutorial,” (Invited paper), Memetic Computing, vol. 4, no. 1, pp. 3–17, Mar. 2012.
Albert Y.S. Lam, Victor O.K. Li, and James J.Q. Yu, “Real-Coded Chemical Reaction Optimization,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 3, pp. 339–353, Jun. 2012.
Albert Y.S. Lam, Victor O.K. Li, and James J.Q. Yu, “Power-Controlled Cognitive Radio Spectrum Allocation with Chemical Reaction Optimization,” IEEE Transactions on Wireless Communications, vol. 12, no. 7, pp. 3180–3190, Jul. 2013.
Albert Y.S. Lam, Victor O.K. Li, and Jin Xu, “On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 5, pp. 605–620, Oct. 2013.