CWCA: Complex-valued encoding water cycle algorithm.

Area of Science:

• Computational intelligence
• Optimization algorithms
• Meta-heuristic computing

Background:

• The meta-heuristic Water Cycle Algorithm (WCA) is widely applied in scientific computation and engineering optimization.
• Existing WCA methods face challenges in exploration, exploitation, convergence speed, and accuracy.

Purpose of the Study:

• To enhance the exploration and exploitation capabilities of the WCA.
• To accelerate the convergence speed of the WCA.
• To improve the calculation accuracy of the WCA.

Main Methods:

• A novel complex-valued encoding Water Cycle Algorithm (CWCA) is proposed.
• River and stream positions are represented using both real and imaginary parts.
• Modified formulas are developed for updating river and stream positions.

Main Results:

• The CWCA demonstrated superior precision compared to the real-valued WCA.
• The CWCA exhibited faster convergence speeds than the real-valued WCA.
• Performance was validated using 12 benchmark functions and four engineering examples.

Conclusions:

• The proposed CWCA effectively improves upon the standard WCA.
• CWCA offers enhanced performance in terms of accuracy and convergence speed.
• This complex-valued approach shows significant potential for optimization tasks.