The surveillance multisensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multisensor placement for indoor surveillance. Our approach is focused on security surveillance of sensible indoor spaces, such as military installations, where distinct security levels can be considered. We propose an evolutionary algorithm to solve the problem, in which a novel special encoding,integer encoding with binary conversion, and effective initialization have been defined to improve the performance and convergence of the proposed algorithm. We also consider the probability of detection for each surveillance point, which depends on the distance to the sensor at hand, to better model real-life scenarios. We have tested the proposed evolutionary approach in different instances of the problem, varying both size and difficulty, and obtained excellent results in terms of the cost of sensors placement and convergence time of the algorithm.

翻译：监控多传感器布局是一个重要的优化问题，其目标是在部署多种不同类型传感器时，最大化对特定区域的覆盖范围，同时最小化部署成本。在本工作中，我们处理该问题的一个改进版本，即面向室内监控的空间分布式多传感器布局。我们的方法侧重于敏感室内空间（如军事设施）的安全监控，其中可以考虑不同的安全等级。我们提出了一种进化算法来解决该问题，其中定义了一种新颖的特殊编码方式——带二进制转换的整数编码，以及有效的初始化策略，以提高所提算法的性能和收敛性。我们还考虑了每个监控点的检测概率，该概率取决于其与当前传感器的距离，从而更好地模拟现实场景。我们已在不同规模和难度的问题实例上测试了所提出的进化方法，并在传感器布局成本和算法收敛时间方面取得了优异的结果。