An Improved Bionic Artificial Lemming Algorithm for Global Optimization and Cloud Task-Scheduling Problems

Area of Science:

• Engineering and Computational Science
• Artificial Intelligence and Machine Learning

Background:

• Intelligent optimization algorithms are crucial for complex engineering and science fields.
• The artificial lemming algorithm (ALA) offers efficient optimization but faces limitations in accuracy and convergence speed for large-scale, complex problems.
• Existing ALA versions exhibit low initial population dispersion and weak exploitation capabilities, hindering optimal solution accuracy and convergence rate.

Purpose of the Study:

• To address the shortcomings of the original artificial lemming algorithm.
• To introduce innovative mechanisms to enhance the algorithm's performance.
• To improve the accuracy and convergence speed of optimization solutions.

Main Methods:

• In-depth analysis of the original artificial lemming algorithm.
• Introduction of novel mechanisms to overcome identified limitations.
• Extensive experimentation using global optimization test problems and cloud scheduling scenarios.

Main Results:

• The improved artificial lemming algorithm shows significantly enhanced performance compared to the original version and baseline algorithms.
• Experimental results indicate superior accuracy and faster convergence speed in solving global optimization problems.
• Successful application to cloud scheduling problems validates the algorithm's practical feasibility and effectiveness.

Conclusions:

• The proposed improvements effectively address the limitations of the original artificial lemming algorithm.
• The enhanced algorithm offers a more accurate and efficient solution for complex optimization and scheduling tasks.
• This work supports the broader application of the improved algorithm in diverse engineering and scientific domains.