The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bioinspired Computing

Background:

• Complex optimization problems require robust and efficient solution methods.
• Existing metaheuristics may not always capture the dynamics of biological systems effectively.
• Bioinspired algorithms offer novel approaches by mimicking natural processes.

Purpose of the Study:

• To introduce the Coral Reefs Optimization (CRO) algorithm, a novel bioinspired approach.
• To evaluate the CRO algorithm's efficacy in solving complex optimization challenges.
• To demonstrate the algorithm's applicability to practical engineering problems.

Main Methods:

• The CRO algorithm simulates a coral reef ecosystem, including coral growth, reproduction, and competition for space.
• Corals represent solutions, and their interactions mimic evolutionary processes.
• The algorithm was tested on continuous and discrete benchmark optimization problems.

Main Results:

• The CRO algorithm exhibited powerful performance in solving hard optimization problems.
• Successful application demonstrated in practical scenarios like mobile network deployment and offshore wind farm design.
• The algorithm's robustness and effectiveness were confirmed across diverse problem types.

Conclusions:

• The Coral Reefs Optimization algorithm is a powerful and robust metaheuristic for complex optimization.
• The bioinspired approach effectively simulates natural processes for problem-solving.
• Further research is warranted for broader real-world applications of the CRO algorithm.