# Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
## 1 Introduction
In the domain of multi-SN-based CPSL, the spiral-surge (SS) method proposed by [PERSON] et al. [18] is a typical behavior-composition method. On the one hand, SS controls the SN currently detecting above-threshold concentrations to move upwind. This is a typical reactive control method because the upwind sprint is directly activated when an odor-detection event occurs. On the other hand, SS employs a deterministic outward spiral movement to steer the SNs when the MSN does not detect any valid concentration information. Moreover, SS uses simple explicit binary communication signals to enable cooperation among the SNs [18]. By combining reactive behavior (i.e., upwind sprint), deterministic spiraling movement, and simple binary communication, the SS method achieves remarkable efficiency in cooperative chemical source localization. However, combining reactive behaviors and intelligent deliberative methods in cooperative CPSL processes requires further investigation. In this paper, we proposed an upwind-adapted genetic algorithm (uGA) that combines the upwind-adapted crossover operator based on the genetic algorithm (GA) [19; 20] and the reactive upwind sprint. The GA is an intelligent optimization algorithm that is traditionally used to search for the global maximum of a fitness function. Empirically, there are some lasting but wandering concentration maxima near the chemical-release source. By searching for the concentration maxima, the GA and other optimization algorithms can be used to solve the CPSL problem. However, unlike the direct calculation of fitness values in numerical optimizations, SNs equipped with common chemical sensors should arrive at an objective location to detect the concentration. It is difficult for SNs to obtain essential concentration detection owing to the intermittent and dynamic distribution of chemicals. Moreover, some local concentration maxima exist at significant distances from the plume source [3]. Thus, solving the CPSL problem is not straightforward. To overcome these difficulties, the uGA attempts to combine the advantages of reactive upwind sprints and an upwind-customized crossover operation based on the GA as follows. First, a customized crossover operator is modeled as the elementary C-behavior. The crossover operator in the GA is customized by restricting the newly generated chromosome to the upwind search area of the chromosome centroid. Second, for an individual SN, the upwind sprint and a deterministic departure from the SN centroid are modeled as elementary U-behavior and D-behavior, respectively. Third, with respect to the different situations in the CPSL process, the three elementary behaviors are combined to form two combinational behaviors. The most commonly used filamentous plume simulation platform (FPSP) [9], which was proposed to assist field experiments by [PERSON] et al. [21], was employed here to compare the performance of the proposed uGA method and the SS method. It is expected that using uGA in the MSN-based CPSL problem can improve the time-efficiency of the CPSL process, which is significant for reducing the loss caused by the targeted pollution. The remainder of this paper is organized as follows. Section 2 presents the details of the proposed uGA method. The simulation setup and results are presented in Sections 3 and 4, respectively. The conclusions are presented in Section 5.