An enhanced whale optimization algorithm for DNA storage encoding.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bioinformatics

Background:

• Metaheuristic algorithms, including the whale optimization algorithm, often suffer from premature convergence to local optima.
• Addressing this limitation is crucial for improving the performance and reliability of optimization techniques.

Purpose of the Study:

• To propose and evaluate an Improved Selective Opposition Whale Optimization Algorithm (ISOWOA) designed to overcome the convergence issues of standard whale optimization algorithms.
• To demonstrate the effectiveness of ISOWOA in designing high-quantity DNA code sets for biological computing applications.

Main Methods:

• The ISOWOA incorporates enhanced quasi-opposition learning (EQOBL) for selective predator position updates and fitness evaluation.
• An improved time-varying strategy is employed for inertia weight and predator position updates, facilitating food source position updates.
• Algorithm performance is rigorously assessed using CEC 2005 and CEC 2015 benchmark functions across various dimensions.

Main Results:

• ISOWOA demonstrated superior performance compared to existing methods on benchmark functions, validated by Wilcoxon's rank sum and Friedman's nonparametric tests.
• In biological computing, ISOWOA achieved equal or superior lower bounds for multi-constraint DNA storage coding sets.
• The algorithm increased the amount of DNA storage codes by 2-18%, highlighting its practical optimization capabilities.

Conclusions:

• The proposed ISOWOA effectively mitigates premature convergence in metaheuristic optimization.
• ISOWOA shows significant promise and reliability for practical optimization tasks, particularly in DNA data storage and biological computing.