A general framework for statistical performance comparison of evolutionary computation algorithms

This paper proposes a statistical methodology for comparing the performance of evolutionary computation algorithms. A two-fold sampling scheme for collecting performance data is introduced, and this data is assessed using a multiple hypothesis testing framework relying on a bootstrap resampling procedure. The proposed method offers a convenient, flexible, and reliable approach to comparing algorithms in a wide variety of applications. KEY WORDS Evolutionary computation, statistics, performanc

An Enhanced Bat Algorithm for Parallel Localization Based on a Mobile Beacon Sensor in Wireless Sensor Networks

International audienceWireless sensor networks (WSNs) have done extensive work over the past decade, focusing primarily on node position in large WSNs when using Internet of Things. When a few anchor nodes are established, the location of sensor nodes must be found and defined in a large network. The goal is to solve this problem by means of an energy-minimal and high-precision protocol. We suggest an improved parallel position scheme based on Bat algorithms, in this article. The main idea is to use a mobile node in the middle of certain network nodes. Wherein, the center node moves along a spiral path to locate its neighboring nodes in parallel. Simulation results of some case studies show remarkable localization quality with regard to accuracy and energy consumption