FPGA Implementation of Floating Point Based Cuckoo Search Algorithm

Abstract

Cuckoo search algorithm (CSA) has been a candidate for numerous recent applications and showed great compatibility in solving optimization problems. It is a metaheuristic algorithm which is based on the odd breeding strategy of the Cuckoo bird spices. It is used to find an optimum or near optimum solution for a certain problem. In this research, we propose an FPGA hardware implementation for the CSA based on single precision IEEE floating point (FP). The FP format provides a wider range and higher precision when compared to fixed point format. To the best of our knowledge, this is the first study to consider implementing FP format-based CSA on FPGA. The proposed design is implemented using pipelined and parallel techniques to get a high throughput and speed. The design is controlled and coordinated using finite state machines (FSMs) modules and is configured on Cyclone IV E FPGA chip from Intel. Three common benchmark functions are used to evaluate the performance of the proposed design. The design has a maximum operating frequency of 99 MHz. It was found out the maximum power consumption for the most complex function is 610.28 mW, mainly due to the use of FP format. In addition, the proposed design is implementation and evaluated for multidimensional operation. Accordingly, the proposed design is suitable for path planning for unmanned aerial vehicles (UAVs), sensor deployments for wireless sensor networks (WSNs) in addition to medical diagnostic and DSP applications. INDEX TERMS Cuckoo search algorithm (CSA), field programmable gate array (FPGA), finite state machine (FSM), IEEE floating point format.

Metaheuristic Optimization Techniques Used in Controlling of an Active Magnetic Bearing System for High-Speed Machining Application

“…However, host birds may discover that these eggs are not their own (with a probability of p a ) and may either destroy or abandon them [57]. This indicates that the number of nests will decrease with each generation, thus it is believed that the hosting bird that abandoned his nest would establish a new nest in a different area therefore maintaining the number of nests constant between generations and introducing variation to the CS algorithm via fresh nest location [58].…”

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“…The primary purpose is to increase the stability and convergence of the method. CSA is also used in many application these includes Power Engineering [64,65], Telecommunication [66,67], Robotics [68], TSP problem [69], Image processing [70,71], embedded systems [72], and etc.…”