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For globally solving sum of linear ratios problem (SLRP), this paper presents a new branch-and-bound method. In this method, a new linear relaxation technique is proposed firstly; then, the initial problem SLRP is solved by a sequence of linear programming problems. Meanwhile, to improve the convergence speed of our algorithm, two accelerating techniques are presented. The proposed algorithm is proved to be convergent, and some experiments are provided to show its feasibility and efficiency. [ABSTRACT FROM AUTHOR]

Aiming at typical shortcomings including large memory occupation of GA(Genetic Algorithm) in evolvable hardware and poor searching capabilities of CGA(Compact Genetic Algorithm) in solving complex problems, this paper presents an improved compact genetic algorithm named ne-TCGA(none-persistent elitism Compact Genetic Algorithm with Tendency). This algorithm is combined with the analysis of convergent trend on the basis of TCGA (Compact Genetic Algorithm with Tendency) and it adopts the strategy of temporary elitist preservation, which both ensure the adequate selection pressure, and maintains the diversity of the population in the evolutionary process. According to the experiment, it can be concluded that the ne-TCGA applied in evolvable hardware has better computational efficiency than other random search algorithms. [ABSTRACT FROM AUTHOR]

In this paper, for solving generalized linear fractional programming (GLFP), a new branch-and-reduce approach is presented. Firstly, an equivalent problem (EP) of GLFP is given; then, a new linear relaxation technique is proposed; finally, the problem EP is reduced to a sequence of linear programming problems by using the new linear relaxation technique. Meanwhile, to improve the convergence speed of our algorithm, two reducing techniques are presented. The proposed algorithm is proved to be convergent, and some experiments are provided to show its feasibility and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, three preconditioned parallel multisplitting methods are established for solving the large sparse linear systems, including local preconditioned parallel multisplitting relaxation method, global preconditioned parallel multisplitting relaxation method and global preconditioned nonstationary parallel multisplitting relaxation method. The convergence and comparison results of the methods associated with USAOR multisplitting are given when the coefficient matrices of the linear systems are H-matrices. We prove three preconditioned parallel multisplitting USAOR method are better than parallel multisplitting USAOR methods for M-matrices linear systems. Finally, numerical examples are given to illustrate the methods are valid. [ABSTRACT FROM AUTHOR]