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.

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.

A New Structural Optimization Method Based on Group Hunting of Animals: Hunting Search (HuS)

2010 ◽  
Author(s):  
R. Oftadeh ◽  
M. J. Mahjoob
Keyword(s):  
Structural Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimization Methods ◽  
Multi Level ◽  
Group Hunting

This paper presents a novel structural optimization algorithm based on group hunting of animals such as lions, wolves, and dolphins. Although these hunters have differences in the way of hunting but they are common in that all of them look for a prey in a group. The hunters encircle the prey and gradually tighten the ring of siege until they catch the prey. In addition, each member of the group corrects its position based on its own position and the position of other members. If the prey escapes from the ring, the hunters reorganize the group to siege the prey again. A benchmark numerical optimization problems is presented to show how the algorithm works. Three benchmark structural optimization problems are also presented to demonstrate the effectiveness and robustness of the proposed Hunting Search (HuS) algorithm for structural optimization. The objective in these problems is to minimize the weight of bar trusses. Both sizing and layout optimization variables are included, too. The proposed algorithm is compared with other global optimization methods such as CMLPSA (Corrected Multi-Level & Multi-Point Simulated Annealing) and HS (Harmony Search). The results indicate that the proposed method is a powerful search and optimization technique. It yields comparable and in some cases, better solutions compared to those obtained using current algorithms when applied to structural optimization problems.

A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization

2016 ◽  
Vol 25 (02) ◽  
pp. 1550030 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Amir H. Alavi ◽  
Suash Deb
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Fine Tuning ◽  
Benchmark Problems ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Multi Stage ◽  
Krill Herd

A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods.

scholarly journals Simulation and Optimization of Control of Selected Phases of Gyroplane Flight

2017 ◽  
Author(s):  
Wienczyslaw Stalewski
Keyword(s):  
Flight Control ◽  
Numerical Optimization ◽  
Stokes Equations ◽  
Optimization Problems ◽  
Computational Simulation ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Approach ◽  
Simulation And Optimization ◽  
Control Procedures

The optimization methods are increasingly used to solve challenging problems of aeronautical engineering. Typically, the optimization methods are utilized in design of aircraft airframe or its structure. The presented study is focused on an improvement of aircraft-flight-control procedures through the numerical optimization approach. The optimization problems concern selected phases of flight of light gyroplane - a rotorcraft using an unpowered rotor in autorotation to develop lift and an engine-powered propeller to provide thrust. An original methodology of computational simulation of rotorcraft flight was developed and implemented. In this approach the aircraft-motion equations are solved step-by-step, simultaneously with the solution of the Unsteady Reynolds-Averaged Navier-Stokes equations, which is conducted to assess aerodynamic forces acting on the aircraft. As a numerical optimization method, the BFGS algorithm was adapted. The developed methodology was applied to optimize the flight-control procedures in selected stages of gyroplane flight in direct proximity of the ground, where properly conducted control of the aircraft is critical to ensure flight safety and performance. The results of conducted computational optimizations proved qualitative correctness of the developed methodology. The research results can be helpful in design of easy-to-control gyroplanes and also in the training of pilots of this type of rotorcraft.

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.

A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems

2013 ◽  
Vol 464 ◽  
pp. 352-357
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computation Time ◽  
Finite Sequence ◽  
Bat Algorithm ◽  
Harmony Search Algorithm ◽  
Response Surfaces ◽  
Sample Mean ◽  
Constrained Problems

The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search process and social behavior of bat echolocation for decision direction. These algorithms were implemented under different natures of three optimization, which are single-peak, multi-peak and curved-ridge response surfaces. Moreover, both algorithms were also applied to constrained engineering problems. The results from non-linear continuous unconstrained functions in the context of response surface methodology and constrained problems can be shown that Bat algorithm seems to be better in terms of the sample mean and variance of design points yields and computation time.

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.

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