Applications and Analysis of Bio-Inspired Eagle Strategy for Engineering Optimization

TL;DR

This paper analyzes randomization techniques in bio-inspired swarm algorithms, especially eagle strategy with Lévy flights, and demonstrates its efficiency in solving complex engineering optimization problems.

Contribution

It provides a detailed analysis of stochastic methods in nature-inspired algorithms and applies eagle strategy with Lévy flights to improve optimization efficiency.

Findings

Eagle strategy with Lévy flights reduces computational efforts.

Randomization techniques influence search performance.

Application to engineering problems shows significant efficiency gains.

Abstract

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 L\'evy flights can perform extremely well in reducing the overall computational efforts.