A novel hybrid wolf pack algorithm with harmony search for global numerical optimization

2017 ◽  
Author(s):  
Xiayang Chen ◽  
Chaojing Tang ◽  
Jian Wang ◽  
Lei Zhang
Keyword(s):  
Numerical Optimization ◽  
Harmony Search ◽  
Global Numerical Optimization

scholarly journals An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Lihong Guo ◽  
Gai-Ge Wang ◽  
Heqi Wang ◽  
Dinan Wang
Keyword(s):  
Numerical Optimization ◽  
Firefly Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Convergence Speed ◽  
Function Optimization ◽  
Global Numerical Optimization ◽  
Running Time ◽  
Hybrid Metaheuristic ◽  
Better Than

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods.

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.

Hybridizing Harmony Search with Biogeography Based Optimization for Global Numerical Optimization

2013 ◽  
Vol 10 (10) ◽  
pp. 2312-2322 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Hong Duan ◽  
Heqi Wang ◽  
Luo Liu ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Harmony Search ◽  
Global Numerical Optimization

Hybridizing harmony search algorithm with cuckoo search for global numerical optimization

2014 ◽  
Vol 20 (1) ◽  
pp. 273-285 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Xiangjun Zhao ◽  
Hai Cheng Eric Chu
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Cuckoo Search ◽  
Harmony Search Algorithm ◽  
Global Numerical Optimization

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 Enhancing the Harmony Search Algorithm Performance on Constrained Numerical Optimization

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 25759-25780 ◽  
Author(s):  
Edgar Alfredo Portilla-Flores ◽  
Alvaro Sanchez-Marquez ◽  
Leticia Flores-Pulido ◽  
Eduardo Vega-Alvarado ◽  
Maria Barbara Calva Yanez ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Algorithm Performance
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