Fuzzy based Quantum Genetic Algorithm for Project Team Formation

2016 ◽  
Vol 12 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Arish Pitchai ◽  
Reddy A. V. ◽  
Nickolas Savarimuthu
Keyword(s):  
Genetic Algorithm ◽  
Personnel Selection ◽  
Optimization Problem ◽  
Selection Process ◽  
Quantum Walk ◽  
Project Team ◽  
Algorithmic Approach ◽  
Quantum Genetic Algorithm ◽  
Requirement Specifications ◽  
The Given

Formation of an effective project team plays an important role in successful completion of the projects in organizations. As the computation involved in this task grows exponentially with the growth in the size of personnel, manual implementation is of no use. Decision support systems (DSS) developed by specialized consultants help large organizations in personnel selection process. Since, the given problem can be modelled as a combinatorial optimization problem, Genetic Algorithmic approach is preferred in building the decision making software. Fuzzy descriptors are being used to facilitate the flexible requirement specifications that indicates required team member skills. The Quantum Walk based Genetic Algorithm (QWGA) is proposed in this paper to identify near optimal teams that optimizes the fuzzy criteria obtained from the initial team requirements. Efficiency of the proposed design is tested on a variety of artificially constructed instances. The results prove that the proposed optimization algorithm is practical and effective.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2017 ◽  
Vol 6 (2) ◽  
pp. 18-37 ◽  
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Complex Plane ◽  
Search Space ◽  
Regions Of Interest ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

Innovative Genetic Algorithmic Approach to Select Potential Patches Enclosing Real and Complex Zeros of Nonlinear Equation

2021 ◽  
pp. 223-243
Author(s):  
Vijaya Lakshmi V. Nadimpalli ◽  
Rajeev Wankar ◽  
Raghavendra Rao Chillarige
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Equation ◽  
Search Space ◽  
Regions Of Interest ◽  
New Method ◽  
Real Numbers ◽  
Algorithmic Approach ◽  
Space Reduction ◽  
Complex Zeros ◽  
The Given

In this article, an innovative Genetic Algorithm is proposed to find potential patches enclosing roots of real valued function f:R→R. As roots of f can be real as well as complex, the function is reframed on to complex plane by writing it as f(z). Thus, the problem now is transformed to finding potential patches (rectangles in C) enclosing z such that f(z)=0, which is resolved into two components as real and imaginary parts. The proposed GA generates two random populations of real numbers for the real and imaginary parts in the given regions of interest and no other initial guesses are needed. This is the prominent advantage of the method in contrast to various other methods. Additionally, the proposed ‘Refinement technique' aids in the exhaustive coverage of potential patches enclosing roots and reinforces the selected potential rectangles to be narrow, resulting in significant search space reduction. The method works efficiently even when the roots are closely packed. A set of benchmark functions are presented and the results show the effectiveness and robustness of the new method.

A Survey of Quantum Genetic Algorithm for Combinatorial Optimization Problems

2014 ◽  
Vol 568-570 ◽  
pp. 822-826 ◽  
Author(s):  
Feng Mei Wei ◽  
Jian Pei Zhang ◽  
Bing Li ◽  
Jing Yang
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Job Shop ◽  
Optimization Problems ◽  
The Other ◽  
Quantum Genetic Algorithm ◽  
Combinatorial Optimization Problems ◽  
Combinational Optimization ◽  
Rotation Strategy

Combined with quantum computing and genetic algorithm, quantum genetic algorithm (QGA) shows considerable ability of parallelism. Experiments have shown that QGA performs quite well on TSP, job shop problem and some other typical combinatorial optimization problems. The other problems like nutritional diet which can be transformed into specific combinational optimization problem also can be solved through QGA smoothly. This paper sums up the main points of QGA for general combinatorial optimization problems. These points such as modeling of the problem, qubit decoding and rotation strategy are useful to enhance the convergence speed of QGA and avoid premature at the same time.

scholarly journals Path Planning for a Space-Based Manipulator System Based on Quantum Genetic Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zhengcang Chen ◽  
Weijia Zhou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Optimization Problem ◽  
Planning Problem ◽  
Positional Error ◽  
End Effector ◽  
Quantum Genetic Algorithm ◽  
Object A ◽  
Floating Base ◽  
Six Degree Of Freedom

In this study, by considering a space-based, n-joint manipulator system as research object, a kinematic and a dynamic model are constructed and the system’s nonholonomic property is discussed. In light of the nonholonomic property unique to space-based systems, a path planning method is introduced to ensure that when an end-effector moves to the desired position, a floating base achieves the expected pose. The trajectories of the joints are first parameterized using sinusoidal polynomial functions, and cost functions are defined by the pose deviation of the base and the positional error of the end-effector. At this stage, the path planning problem is converted into a target optimization problem, where the target is a function of the joints. We then adopt a quantum genetic algorithm (QGA) to solve this objective optimization problem to attain the optimized trajectories of the joints and then execute nonholonomic path planning. To test the proposed method, we carried out a simulation on a six-degree-of-freedom (DOF) space-based manipulator system (SBMS). The results showed that, compared to traditional genetic optimization algorithms, the QGA converges more rapidly and has a more accurate output.

scholarly journals An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Xian Zhang ◽  
Xiao-Yi Qian ◽  
Hui-Deng Peng ◽  
Jian-Hui Wang
Keyword(s):  
Genetic Algorithm ◽  
Global Convergence ◽  
Convergence Rate ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Continuous Optimization ◽  
Evolutionary Process ◽  
Experimental Results ◽  
Quantum Genetic Algorithm

For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. AndHεgate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved byMarkovchain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy.

Optimal Placement Strategy of DHT-Based Systems to Minimize Lookup Latency

2013 ◽  
Vol 760-762 ◽  
pp. 178-184
Author(s):  
Tai Zhang ◽  
Sheng Wang ◽  
Dan Liao
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Main Idea ◽  
Physical Space ◽  
Optimal Placement ◽  
Logical Space ◽  
Traffic Matrix ◽  
The Given

This paper proposes an optimal placement strategy of physical nodes of DHT-based systems to minimize the lookup latency and improve the throughput of system. The main idea of our approach is to create a new hierarchy named link space between logical space and physical space of DHT overlay. We firstly give an assignment of link space and then present the optimal placement strategy of placing physical nodes on it based on the given lookup traffic matrix. At last, we use genetic algorithm to solve the optimization problem.

scholarly journals Quantum Genetic Algorithm Based on Qutrits and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Valerii Tkachuk
Keyword(s):  
Genetic Algorithm ◽  
Quantum System ◽  
Process Simulation ◽  
Evolutionary Process ◽  
Standard Test ◽  
Point Of View ◽  
Computational Power ◽  
Test Functions ◽  
Quantum Genetic Algorithm ◽  
The Given

Typical approaches to designing quantum genetic algorithms are based on a concept of a qubit, a two-level quantum system. But many-valued quantum logic is more perspective from the point of view of the computational power. This paper proposes a quantum genetic algorithm based on a three-level quantum system in order to accelerate evolutionary process. Simulation using a set of standard test functions proves that the given algorithm is more effective and precise than the conventional quantum genetic algorithm.

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