Focusing on the unbalance issue between local optimization and global optimization and the inability to jump out of the local optimum of Artificial Fish Swarm Algorithm （AFSA）， an Adaptive AFSA utilizing Gene Exchange （AAFSA-GE） was proposed. Firstly， an adaptive mechanism of view and step was utilized to enhance the search speed and accuracy. Then， chaotic behavior and gene exchange behavior were employed to improve the ability of jumping out of the local optimum and the search efficiency. Ten classic test functions were selected to prove the feasibility and robustness of the proposed algorithm by comparing it with the other three modified AFSAs， which are Normative Fish Swarm Algorithm （NFSA）， FSA optimized by PSO algorithm with Extended Memory （PSOEM-FSA）， and Comprehensive Improvement of Artificial Fish Swarm Algorithm （CIAFSA）. Experimental results show that AAFSA-GE achieves better results in local and global search ability than those of PSOEM-FSA and CIAFSA，and better search efficiency and better global search ability than those of NSFA.

This paper proposed a Low-Power oriented cache Design (LPD) of Level 2 (L2) cache for multi-core processors. LPD considered three different ways to reduce the power consumption while promising the best performance: Low Power oriented Hybrid cache Partition algorithm (LPHP), Cache Reconfiguration Algorithm (CRA), and Way-Prediction based on L2 cache Partition algorithm (WPP-L2). LPHP and CRA closed the columns that were not in use dynamically. WPP-L2 predicted one appropriate way before cache accesses, which could save the access time, so as to save power. These three methods of LPD saved power consumption by 20.5%, 17% and 64.6% on average over the traditional Least Recently Used (LRU) strategy with improvement of the throughput and little influence on the runtime of programs. The experimental results show that this method can reduce the power of multi-core processors significantly and maintain the system performance.