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    Area of Science:

    • Computational Intelligence
    • Operations Research
    • Multi-objective Optimization

    Background:

    • Chemical Reaction Optimization (CRO) is a powerful metaheuristic for mono-objective problems.
    • Existing metaheuristics often struggle with problems involving multiple conflicting objectives.

    Purpose of the Study:

    • To develop a multiobjective variant of Chemical Reaction Optimization (CRO).
    • To enhance the efficiency and effectiveness of metaheuristics for multi-objective optimization problems.

    Main Methods:

    • Proposed Nondominated Sorting Chemical Reaction Optimization (NSCRO).
    • Developed a novel quasi-linear time complexity quick nondominated sorting algorithm.
    • Conducted experimental comparisons against established multi-objective algorithms.

    Main Results:

    • NSCRO demonstrated effectiveness in solving multi-objective optimization problems.
    • The algorithm achieved a well-converged and well-diversified Pareto front approximation.
    • The proposed sorting algorithm improved computational efficiency.

    Conclusions:

    • NSCRO is an effective and efficient approach for multi-objective optimization.
    • The integration of a fast nondominated sorting algorithm enhances computational performance.
    • This work advances the application of metaheuristics to complex optimization challenges.