Chemical-Reaction-Inspired Metaheuristic for Optimization

• Published in: IEEE transactions on evolutionary computation Vol. 14; no. 3; pp. 381 - 399
• Main Authors: Lam, Albert Y S, Li, Victor O K
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Approximation; Artificial intelligence; Benchmark testing; Benchmarking; Buildings; Chemical reaction; Chemical reactions; Chemicals; Cities and towns; Combinatorial analysis; Computer science; control theory; systems; Computer simulation; Cost function; Electrical engineering; Encounters; Exact sciences and technology; Heuristic; Interference; Learning and adaptive systems; Mathematical models; metaheuristics; nature-inspired algorithms; NP-hard problem; Optimization; Optimization methods; Power generation economics; Studies; Theoretical computing; Probabilistic approach; Combinatorial problem; Evolutionary algorithm; Competitiveness; metaheuristics; optimization methods; Combinatorial optimization; Non determinism; Polynomial time; Low energy; Biomimetics; Chemical reaction; Heuristic method; Problem solving; nature-inspired algorithms; Metamodel; Mathematical programming
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2009.2033580

We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches.