Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.     

基于权值编码的组合拍卖竞胜标确定启发式算法研究

针对较大规模组合拍卖竞胜标确定问题(WDP),提出了基于权值编码的竞胜标确定启发式算法.改进了算法编码机制并嵌入基于WDP本质特点的启发式搜索规则,极大地提高了算法进化能力和求解效率.模拟实验结果表明该算法能够在较短时间内求出WDP最优解或满意近似解,为较大规模网上组合拍卖竞胜标确定问题提供了切实可行的求解算法.     

基于主成分分析的网络搜索数据合成方法研究

针对利用网络搜索数据合成各类指数时难以消除共线性、难以合理确定各指标权重等问题,基于数据降维的思想构建了以主成分分析法为核心的指数合成方法,并以利用网络搜索数据合成消费者物价指数为例,将提出的方法与当前主流的逐步回归法做出比较研究.研究结果表明,基于主成分分析法的数据合成方法可以得到稳定性和拟合度更高的指数.     

Polynomiality of an inexact infeasible interior point algorithm for semidefinite programming

In this paper we present a primal-dual inexact infeasible interior-point algorithm for semidefinite programming problems (SDP). This algorithm allows the use of search directions that are calculated from the defining linear system with only moderate accuracy, and does not require feasibility to be maintained even if the initial iterate happened to be a feasible solution of the problem. Under a mild assumption on the inexactness, we show that the algorithm can find an -approximate solution of an SDP in O(n²ln(1/)) iterations. This bound of our algorithm is the same as that of the exact infeasible interior point algorithms proposed by Y. Zhang.Research supported in part by the Singapore-MIT alliance, and NUS Academic Research Grant R-146-000-032-112.Mathematics Subject Classification (1991): 90C05, 90C30, 65K05     

LINEAR SEARCH FOR A BROWNIAN TARGET MOTION

A target is assumed to move according to a Brownian motion on the real line. The searcher starts from the origin and moves in the two directions from the starting point.The object is to detect the target. The purpose of this paper is to find the conditions under which the expected value of the first meeting time of the searcher and the target is finite,and to show the existence of a search plan which made this expected value minimum.     

Discovery of a Novel 5-HT_(2A) Inhibitor by Pharmacophore-based Virtual Screening

The serotonin 2A(5-HT2A) receptor has been implicated in several neurological conditions and potent 5-HT2A antagonists have therapeutic effects in the treatment of schizo phrenia and depression.In this study,a potent novel 5-HT2A inhibitor 05245768 with a Ki value of (593.89±34.10) nmol/L was discovered by integrating a set of computational approaches and experiments(protein structure prediction,pharmacophore-based virtual screening,automated molecular docking and pharmacological bioassay).The 5-HT2A recept...     

A neighborhood search heuristic for pre-marshalling export containers

In a yard where export containers are piled up, only those on the top are directly accessible to the stacking equipment. As a result, extra rehandles may occur when lifting them up for loading onto ships. One way to improve operational efficiency is to pre-marshal the containers in such an order that it fits the loading sequence. This paper proposes a model to develop a movement plan to improve the layout of containers in a bay. The proposed heuristic consists of a neighborhood search process, an integer programming model, and three minor subroutines. Each of the components plays a different role in the heuristic. Several sets of testing results demonstrate the performance of the heuristic as well as the contributions of the components.     

Finding dominators via disjoint set union

The problem of finding dominators in a directed graph has many important applications, notably in global optimization of computer code. Although linear and near-linear-time algorithms exist, they use sophisticated data structures. We develop an algorithm for finding dominators that uses only a “static tree” disjoint set data structure in addition to simple lists and maps. The algorithm runs in near-linear or linear time, depending on the implementation of the disjoint set data structure. We give several versions of the algorithm, including one that computes loop nesting information (needed in many kinds of global code optimization) and that can be made self-certifying, so that the correctness of the computed dominators is very easy to verify.     

Discrete space location-allocation solutions from genetic algorithms

Genetic algorithms are adaptive sampling strategies based on information processing models from population genetics. Because they are able to sample a population broadly, they have the potential to out-perform existing heuristics for certain difficult classes of location problems. This paper describes reproductive plans in the context of adaptive optimization methods, and details the three genetic operators which are the core of the reproductive design. An algorithm is presented to illustrate applications to discrete-space location problems, particularly thep-median. The algorithm is unlikely to compete in terms of efficiency with existingp-median heuristics. However, it is highly general and can be fine-tuned to maximize computational efficiency for any specific problem class.