# An improved crayfish optimization algorithm for solving engineering optimization problems

## TL;DR

This paper introduces an improved crayfish optimization algorithm that enhances performance in solving complex engineering optimization problems.

## Contribution

The paper proposes an improved crayfish optimization algorithm (ICOA) with strategies to enhance diversity and exploration capabilities.

## Key findings

- ICOA outperforms COA in five engineering optimization problems with improvements ranging from 0.01% to 88.8%.
- The use of Sobol sequence mapping and Lévy flight strategy improves population diversity and exploration.
- Statistical analysis confirms ICOA's effectiveness on benchmark functions and real-world problems.

## Abstract

By emulating crayfish behaviors such as social foraging, rapid retreat from threats, and adaptive sensing, the Crayfish Optimization Algorithm (COA) achieves a dynamic balance between global search and local exploration, improving optimization efficiency. However, COA suffers from diversity degradation, insufficient exploration capability, low optimization finding accuracy, and easy fall to a local minimum. To solve these problems, an improved crayfish optimization algorithm (ICOA) is proposed. Firstly, the position of the population is achieved through the application of the Sobol sequence mapping in the initialization phrase, which enhances the diversity within the population. Secondly, a Lévy flight strategy is proposed in the foraging phase, which avoids algorithms fall into local optimization and enhances the individuals’ capacity for extensive exploration within the solution space. Subsequently, during the competition phase, using the Euclidean distance-fitness balanced competition strategy improves simultaneous development and exploration performance. To evaluate ICOA performance, the IEEE CEC2019 and CEC2020 benchmark functions and experiments were used in different dimensions for verification, followed by sensitivity analysis, quantitative analysis, and nonparametric statistical analysis. Furthermore, the effectiveness is validated in five engineering optimization problems, in which ICOA improved by 0.28%, 17.86%, 0.01%, 88.8% and 0.1%, respectively, compared to COA. ICOA exhibits enhanced optimization capabilities to tackle complex spatial and practical challenges. Incorporating multiple strategies markedly improve the efficacy of ICOA. This finding has significant implications in the field of engineering optimizations.