Artificial algae algorithm with multi-light source for numerical optimization and applications.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• The Artificial Algae Algorithm (AAA) is a bio-inspired optimization technique simulating microalgae behavior.
• AAA utilizes helical movement for colony modification, balancing exploration and exploitation.

Purpose of the Study:

• To enhance the Artificial Algae Algorithm (AAA) by introducing a multi-light source movement (MLS) strategy.
• To improve the balance between exploration and exploitation in optimization processes.

Main Methods:

• Developed the Artificial Algae Algorithm with Multi-Light Source (AAAML) by incorporating distinct light sources for each dimension.
• Employed a tournament selection method to choose diverse light sources, guiding the search space exploration.
• Evaluated AAAML performance on IEEE-CEC'13 benchmark and IEEE-CEC'11 real-world optimization problems.

Main Results:

• The AAAML algorithm demonstrated superior performance compared to the original AAA and other state-of-the-art algorithms.
• Multi-light source movement (MLS) significantly enhanced the success rate and efficiency of the Artificial Algae Algorithm.
• Experimental results confirmed the effectiveness of AAAML in both benchmark and real-world optimization scenarios.

Conclusions:

• The proposed AAAML, with its multi-light source movement, offers a robust improvement over the standard AAA.
• This enhanced algorithm provides a better balance between exploration and exploitation for complex optimization tasks.
• AAAML represents a significant advancement in bio-inspired optimization algorithms for scientific and engineering applications.