Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Optimization
This work addresses the need for better understanding of randomization in optimization algorithms for engineering design problems, but it appears incremental as it builds on existing eagle strategy and analysis methods.
The paper tackled the problem of understanding why some randomization techniques in swarm-intelligence optimization algorithms perform better than others by analyzing these methods in nature-inspired algorithms and applying eagle strategy with Lévy flights to four engineering design benchmarks, resulting in extremely well performance in reducing overall computational efforts.
All swarm-intelligence-based optimization algorithms use some stochastic components to increase the diversity of solutions during the search process. Such randomization is often represented in terms of random walks. However, it is not yet clear why some randomization techniques (and thus why some algorithms) may perform better than others for a given set of problems. In this work, we analyze these randomization methods in the context of nature-inspired algorithms. We also use eagle strategy to provide basic observations and relate step sizes and search efficiency using Markov theory. Then, we apply our analysis and observations to solve four design benchmarks, including the designs of a pressure vessel, a speed reducer, a PID controller and a heat exchanger. Our results demonstrate that eagle strategy with Lévy flights can perform extremely well in reducing the overall computational efforts.