scholarly journals Multi-Scale Quantum Harmonic Oscillator Algorithm With Truncated Mean Stabilization Strategy for Global Numerical Optimization Problems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 18926-18939
Author(s):  
Xinggui Ye ◽  
Peng Wang ◽  
Gang Xin ◽  
Jin Jin ◽  
Yan Huang
Keyword(s):  
Harmonic Oscillator ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Quantum Harmonic Oscillator ◽  
Global Numerical Optimization ◽  
Multi Scale

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

scholarly journals Optimization of High-Dimensional Functions through Hypercube Evaluation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Mustafa Tunay
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Evaluation Process ◽  
Stochastic Search ◽  
High Dimensional ◽  
Potential Candidate ◽  
Objective Functions ◽  
Global Numerical Optimization ◽  
Search Area

A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions.

scholarly journals A Binary Multi-Scale Quantum Harmonic Oscillator Algorithm for 0–1 Knapsack Problem With Genetic Operator

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137251-137265 ◽  
Author(s):  
Yan Huang ◽  
Peng Wang ◽  
Jianping Li ◽  
Xiuhong Chen ◽  
Tao Li
Keyword(s):  
Harmonic Oscillator ◽  
Knapsack Problem ◽  
Quantum Harmonic Oscillator ◽  
Genetic Operator ◽  
Multi Scale

scholarly journals A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
Optimization Approach ◽  
Global Numerical Optimization ◽  
Hybrid Metaheuristic ◽  
Real World Applications

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

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