A Sophisticated Architecture for Evolutionary Multiobjective Optimization Utilizing High Performance DSP

Constructing an evolutionary engine platform in evolvable hardware (EHW) is one of the most important topics, and a sophisticated architecture for the application of adaptive hardware is the key for the platform. In real world, most applications are multi-objective, and it is much necessary to solve the multi-objective problems (MOPs) by implementing evolutionary multi-objective optimization (EMO) in a special hardware platform. At present, there are far fewer attempts concerned with the theme. In this paper, we present an adaptive hardware platform to implement EMO algorithms utilizing high-performance digital signal processor (DSP) device. In this design, we mainly solve the problem of speedup in execution of evolutionary search by using parallel construct to implement such an EMO algorithm on DSP. Experimental results show that our platform works quite well. We still get a speedup of nearly 100 times in the condition that the CPU host frequency is 1810MHz and the hardware clock frequency is 150MHz, which offers an idea that by using a higher frequency DSP, we will get a better speedup, and we may further solve the real-world MOPs in real time.