An aphid inspired metaheuristic optimization algorithm and its application to engineering

Area of Science:

• Computational intelligence
• Optimization algorithms
• Bio-inspired computing

Background:

• Metaheuristic algorithms are inspired by natural behaviors to find optimal solutions.
• These algorithms are widely applied across various scientific and engineering domains.
• Existing algorithms may have limitations in efficiency and adaptability.

Purpose of the Study:

• To introduce a novel bio-inspired metaheuristic algorithm named the Aphids Optimization Algorithm (AOA).
• To simulate the natural foraging behavior of winged aphids, including distinct phases of flight and attack.
• To develop and present optimization models corresponding to these simulated aphid behaviors.

Main Methods:

• The Aphids Optimization Algorithm (AOA) simulates aphid foraging, encompassing winged aphid generation, flight mood, and attack mood.
• The flight mood phase involves adaptive migration strategies influenced by energy and airflow.
• The attack mood phase utilizes simulated senses (smell and vision) for locating and moving towards food sources.

Main Results:

• Experimental results on benchmark test functions demonstrate the efficacy of the AOA.
• The AOA was tested on two classical engineering design problems, validating its practical applicability.
• Comparative analysis indicates that the AOA outperforms other existing metaheuristic algorithms in efficiency.

Conclusions:

• The proposed Aphids Optimization Algorithm (AOA) is an effective novel bio-inspired optimization technique.
• The AOA's simulation of aphid foraging provides a unique and efficient approach to problem-solving.
• The algorithm shows significant potential for application in diverse optimization tasks and engineering design.