An Improved Artificial Fish Swarm Algorithm for Solving the Problem of Investigation Path Planning

TL;DR

This paper introduces a hybrid chaotic artificial fish swarm algorithm enhanced with differential evolution techniques to effectively solve investigation path planning modeled as a multi-traveling salesman problem, improving optimization accuracy and search efficiency.

Contribution

It proposes a novel hybrid algorithm DE-CAFSA that combines chaos theory, adaptive parameters, and differential evolution to address limitations of traditional artificial fish swarm algorithms.

Findings

DE-CAFSA outperforms existing algorithms on multiple datasets

The hybrid approach improves optimization accuracy and search performance

Experimental results validate the effectiveness of the proposed method

Abstract

Informationization is a prevailing trend in today's world. The increasing demand for information in decision-making processes poses significant challenges for investigation activities, particularly in terms of effectively allocating limited resources to plan investigation programs. This paper addresses the investigation path planning problem by formulating it as a multi-traveling salesman problem (MTSP). Our objective is to minimize costs, and to achieve this, we propose a chaotic artificial fish swarm algorithm based on multiple population differential evolution (DE-CAFSA). To overcome the limitations of the artificial fish swarm algorithm, such as low optimization accuracy and the inability to consider global and local information, we incorporate adaptive field of view and step size adjustments, replace random behavior with the 2-opt operation, and introduce chaos theory and sub-optimal solutions to enhance optimization accuracy and search performance. Additionally, we integrate the differential evolution algorithm to create a hybrid algorithm that leverages the complementary advantages of both approaches. Experimental results demonstrate that DE-CAFSA outperforms other algorithms on various public datasets of different sizes, as well as showcasing excellent performance on the examples proposed in this study.