Heart rate optimizer: a novel bio-inspired metaheuristic algorithm.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• Real-world optimization problems require robust and adaptive metaheuristics.
• Existing algorithms often face challenges like premature convergence and poor handling of high-dimensional spaces.
• The human cardiovascular system exhibits adaptive regulation mechanisms relevant to optimization.

Purpose of the Study:

• To introduce a novel bio-inspired metaheuristic, the Heart Rate Optimizer (HRO).
• To enhance optimization performance by modeling cardiovascular and autonomic nervous system dynamics.
• To improve exploration-exploitation balance and population diversity.

Main Methods:

• The Heart Rate Optimizer (HRO) algorithm is proposed, inspired by the human cardiovascular system.
• HRO incorporates tachycardia for global exploration, bradycardia for local exploitation, and Lévy flight-based behavior to escape local optima.
• An Orthogonal Learning strategy is integrated to manage exploration-exploitation dynamics and maintain population diversity.

Main Results:

• HRO demonstrated superior solution accuracy and faster convergence on IEEE CEC2017 and CEC2022 benchmark suites.
• The algorithm showed improved stability compared to nine state-of-the-art metaheuristics.
• HRO's effectiveness was validated on engineering design problems like welded beam and pressure vessel optimization.

Conclusions:

• The Heart Rate Optimizer (HRO) offers a robust and adaptive approach to complex optimization.
• HRO effectively balances exploration and exploitation, enhancing performance and stability.
• The bio-inspired HRO algorithm shows significant promise for both theoretical and practical optimization challenges.