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Informationization is a prevailing trend in today's world. The increasingdemand for information in decision-making processes poses significantchallenges for investigation activities, particularly in terms of effectivelyallocating limited resources to plan investigation programs. This paperaddresses the investigation path planning problem by formulating it as amulti-traveling salesman problem (MTSP). Our objective is to minimize costs,and to achieve this, we propose a chaotic artificial fish swarm algorithm basedon multiple population differential evolution (DE-CAFSA). To overcome thelimitations of the artificial fish swarm algorithm, such as low optimizationaccuracy and the inability to consider global and local information, weincorporate adaptive field of view and step size adjustments, replace randombehavior with the 2-opt operation, and introduce chaos theory and sub-optimalsolutions to enhance optimization accuracy and search performance.Additionally, we integrate the differential evolution algorithm to create ahybrid algorithm that leverages the complementary advantages of bothapproaches. Experimental results demonstrate that DE-CAFSA outperforms otheralgorithms on various public datasets of different sizes, as well as showcasingexcellent performance on the examples proposed in this study.

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