Impacts of Multiple Solutions on the Lackadaisical Quantum Walk Search Algorithm

2020 ◽  
pp. 122-135
Author(s):  
Jonathan H. A. de Carvalho ◽  
Luciano S. de Souza ◽  
Fernando M. de Paula Neto ◽  
Tiago A. E. Ferreira
Keyword(s):  
Multiple Solutions ◽  
Search Algorithm ◽  
Quantum Walk

scholarly journals Quantum walk search algorithm based on phase matching and circuit cmplementation

2015 ◽  
Vol 64 (24) ◽  
pp. 240301
Author(s):  
Chen Han-Wu ◽  
Li Ke ◽  
Zhao Sheng-Mei
Keyword(s):  
Search Algorithm ◽  
Quantum Walk ◽  
Phase Matching

scholarly journals Planning for Multiple Preferences versus Planning with No Preference

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Daniel Bryce
Keyword(s):  
Multiple Solutions ◽  
Search Algorithm ◽  
Resource Consumption ◽  
Decision Makers ◽  
Relative Performance ◽  
Empirical Comparison ◽  
Multiple Alternative ◽  
Nondominated Solution ◽  
Single Objective

Many planning applications must address conflicting plan objectives, such as cost, duration, and resource consumption, and decision makers want to know the possible tradeoffs. Traditionally, such problems are solved by invoking a single-objective algorithm (such as A*) on multiple, alternative preferences of the objectives to identify nondominated plans. The less-popular alternative is to delay such reasoning and directly optimize multiple plan objectives with a search algorithm like multiobjective A* (MOA*). The relative performance of these two approaches hinges upon the number of -values computed for individual search nodes. A* may revisit a node several times and compute a different -value each time. MOA* visits each node once and may compute some number of -values (each estimating the value of a different nondominated solution constructed from the node). While A* does not share -values between searches for different solutions, MOA* can sometimes find multiple solutions while computing a single -value per node. The results of extensive empirical comparison show that (i) the performance of multiple invocations of a single-objective A* versus a single invocation of MOA* is often worse in time and quality and (ii) that techniques for balancing per node cost and exploration are promising.

Multiobjective Optimization of Bioethanol Production via Hydrolysis Using Hopfield-Enhanced Differential Evolution

2014 ◽  
pp. 340-359 ◽  
Author(s):  
T. Ganesan ◽  
I. Elamvazuthi ◽  
K. Z. K. Shaari ◽  
P. Vasant
Keyword(s):  
Differential Evolution ◽  
Multiple Solutions ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Pareto Frontier ◽  
Weighted Sum ◽  
Bioethanol Production ◽  
Hypervolume Indicator

Many industrial problems in process optimization are Multi-Objective (MO), where each of the objectives represents different facets of the issue. Thus, having in hand multiple solutions prior to selecting the best solution is a seminal advantage. In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). These methods are then employed to trace the approximate Pareto frontier to the bioethanol production problem. The Hypervolume Indicator (HVI) is applied to gauge the capabilities of each algorithm in approximating the Pareto frontier. Some comparative studies are then carried out with the algorithms developed in this chapter. Analysis on the performance as well as the quality of the solutions obtained by these algorithms is shown here.

An Improve Cuckoo Search Algorithm for Traveling Salesman Problems

2014 ◽  
Vol 651-653 ◽  
pp. 2291-2295
Author(s):  
Suo Nan Lengzhi ◽  
Yue Guang Li
Keyword(s):  
Multiple Solutions ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traveling Salesman ◽  
Experimental Results ◽  
Traveling Salesman Problems ◽  
Position Value ◽  
Benchmark Instances ◽  
Simulation Results

In this paper, according to the characteristics of TSP. An improve Cuckoo Search Algorithm was used to solve the TSP, adopting the code rule of randomized key representation based on the smallest position value. The experimental results show that the new algorithm is successful in locating multiple solutions and has better accuracy, simulation results of benchmark instances validate the efficiency and superiority of Cuckoo Search Algorithm.

Quantum walk search algorithm for multi-objective searching with iteration auto-controlling on hypercube

2021 ◽  
Author(s):  
Yao-Yao Jiang ◽  
Peng-Chen Chu ◽  
Wen-Bin Zhang ◽  
Hong-Yang Ma
Keyword(s):  
Search Algorithm ◽  
Quantum Walk ◽  
Multi Objective

Parametric Quantum Search Algorithm as Quantum Walk: A Quantum Simulation

2016 ◽  
Vol 77 (1) ◽  
pp. 105-128 ◽  
Author(s):  
Demosthenes Ellinas ◽  
Christos Konstandakis
Keyword(s):  
Search Algorithm ◽  
Quantum Walk ◽  
Quantum Simulation ◽  
Quantum Search ◽  
Quantum Search Algorithm

scholarly journals The quantum walk search algorithm: factors affecting efficiency

2018 ◽  
Vol 29 (3) ◽  
pp. 389-429 ◽  
Author(s):  
NEIL B. LOVETT ◽  
MATTHEW EVERITT ◽  
ROBERT M. HEATH ◽  
VIV KENDON
Keyword(s):  
Efficient Algorithm ◽  
Search Algorithm ◽  
Numerical Study ◽  
Strong Dependence ◽  
Quantum Walk ◽  
Spatial Dimension ◽  
Current Paper ◽  
Individual State ◽  
Factors Affecting ◽  
Underlying Substrate

We carry out a numerical study of the quantum walk search algorithm of Shenvi, Kempe and Whaley Shenvi et al. (2003) and the factors that affect its efficiency in finding an individual state from an unsorted set. Previous work has focused purely on the effects of the dimensionality of the dataset to be searched. In the current paper we consider the effects of interpolating between dimensions, the connectivity of the dataset and the possibility of disorder in the underlying substrate: all these factors affect the efficiency of the search algorithm. We show that in addition to the strong dependence on the spatial dimension of the structure to be searched, there are also secondary dependencies on the connectivity and symmetry of the lattice, with greater connectivity providing a more efficient algorithm. We also show that the algorithm can tolerate a non-trivial level of disorder in the underlying substrate.

An Improve Cuckoo Search Algorithm for Solving Nonlinear Equation Group

2014 ◽  
Vol 651-653 ◽  
pp. 2121-2124
Author(s):  
Rui Hong Zhou ◽  
Yue Guang Li
Keyword(s):  
Nonlinear Equation ◽  
Nonlinear Equations ◽  
Multiple Solutions ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Experimental Results ◽  
Systems Of Nonlinear Equations ◽  
Equation Group

In this paper, according to the characteristics of nonlinear equations. An improve Cuckoo Search Algorithm was used to solve the systems of nonlinear equations, the algorithm was experimented and the experimental results show that the new algorithm to be successful in locating multiple solutions and better accuracy. At the end the paper made a simple comparison with the bat algorithms.

Cuckoo Search Algorithm for Solving Ill-Conditioned Linear Equations

2014 ◽  
Vol 1049-1050 ◽  
pp. 1367-1370
Author(s):  
Yu Feng Ma ◽  
Yue Guang Li
Keyword(s):  
Multiple Solutions ◽  
Search Algorithm ◽  
Linear Equations ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Experimental Results ◽  
Traditional Methods

In this paper, according to the characteristics of ill-conditioned linear equations. A Cuckoo Search Algorithm was used to solve the systems of ill-conditioned linear equations, the algorithm was experimented and the experimental results show that the algorithm to be successful in locating multiple solutions and better accuracy. At the end the paper made a simple comparison with the traditional methods..

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