Adaptive cuckoo search algorithm for unconstrained optimization

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The cuckoo search algorithm (CSA) is a metaheuristic optimization technique inspired by the brood parasitism of cuckoos.
• Standard CSA may face challenges in balancing exploration and exploitation, potentially leading to slower convergence or premature local optima.
• Enhancing existing algorithms is crucial for advancing computational intelligence and solving complex optimization problems.

Purpose of the Study:

• To modify and improve the intensification and diversification strategies within the cuckoo search algorithm (CSA).
• To introduce an adaptive step size adjustment mechanism to accelerate convergence towards global optimal solutions.
• To validate the effectiveness of the proposed enhanced CSA against standard benchmark optimization functions.

Main Methods:

• Modification of the intensification and diversification components of the standard cuckoo search algorithm (CSA).
• Implementation of an adaptive step size adjustment strategy within the CSA framework.
• Performance evaluation using a suite of standard benchmark optimization functions.

Main Results:

• The enhanced cuckoo search algorithm (CSA) with adaptive step size adjustment exhibited faster convergence properties.
• The proposed algorithm demonstrated marked improvements in solution quality compared to the standard CSA across all tested benchmark functions.
• The adaptive strategy effectively balanced exploration and exploitation, leading to more robust optimization.

Conclusions:

• The integration of adaptive step size adjustment significantly enhances the performance of the cuckoo search algorithm (CSA).
• The modified CSA offers a more efficient and effective approach for finding global optimal solutions in complex optimization problems.
• The proposed enhancements provide a valuable contribution to the field of metaheuristic optimization.