Outline
- Abstract
- Keywords
- 1. Introduction
- 2. Cuckoos and Their Special Lifestyle for Reproduction
- 3. the Proposed Cuckoo Optimization Algorithm (coa)
- 3.1. Generating Initial Cuckoo Habitat
- 3.2. Cuckoos’ Style for Egg Laying
- 3.3. Immigration of Cuckoos
- 3.4. Eliminating Cuckoos in Worst Habitats
- 3.5. Convergence
- 4. Benchmarks on Cuckoo Optimization Algorithm
- 4.1. Test Cost Functions
- 4.2. Multivariable Controller Design
- 4.2.1. Pid Controller for Mimo Processes
- 4.2.2. Evolutionary Pid Design
- 5. Conclusions
- References
رئوس مطالب
- چکیده
- کلیدواژه ها
- 1.مقدمه
- 2.کوکوها و سبک زندگی خاص آن‌ها برای تولید مثل
- 3.الگوریتم پیشنهادی بهینه‌سازی کوکو (COA)
- 3.1.ساخت زیستگاه اولین کوکوها
- 3.2. روش تخم‌گذاری کوکوها
- 3.3. مهاجرت کوکوها
- 3.4. نابودسازی کوکوهای ساکن در بدترین زیستگاه‌ها
- 3.5. همگرایی
- 4. محک‌های الگوریتم بهینه‌سازی کوکو
- 4.1. توابع هزینه آزمایش
- 4.2. طراحی کنترل‌کننده چند متغیره
- 4.2.1. کنترل‌کننده PID برای فرآیندهای چند ورودی چند خروجی
- 4.2.2. طراحی تکاملی PID
- 5. نتیجه‌گیری
Abstract
In this paper a novel evolutionary algorithm, suitable for continuous nonlinear optimization problems, is introduced. This optimization algorithm is inspired by the life of a bird family, called Cuckoo. Special lifestyle of these birds and their characteristics in egg laying and breeding has been the basic motivation for development of this new evolutionary optimization algorithm. Similar to other evolutionary methods, Cuckoo Optimization Algorithm (COA) starts with an initial population. The cuckoo population, in different societies, is in two types: mature cuckoos and eggs. The effort to survive among cuckoos constitutes the basis of Cuckoo Optimization Algorithm. During the survival competition some of the cuckoos or their eggs, demise. The survived cuckoo societies immigrate to a better environment and start reproducing and laying eggs. Cuckoos’ survival effort hopefully converges to a state that there is only one cuckoo society, all with the same profit values. Application of the proposed algorithm to some benchmark functions and a real problem has proven its capability to deal with difficult optimization problems.
Conclusions
In this paper, a new optimization algorithm was proposed which was inspired by lifestyle of a bird called Cuckoo. Special characteristics of cuckoos in egg laying and breeding had been the basic motivation for development of this new optimization algorithm. Each individual in the algorithm has a habitat around which she starts to lay eggs. In case the eggs survive, they grow and become mature cuckoos. Then for reproduction purposes cuckoos migrate toward best habitat, found up to now. The diversion occurred when moving toward goal habitat makes the population search more area than the case population moves straight forward on a line. After some immigrants all cuckoo population gather the same habitat which is the area’s best position. The introduced algorithm was tested on 5 benchmark cost functions. The comparison of COA with standard versions of PSO and GA with Roulette wheel selection, uniform crossover, showed the superiority of COA in fast convergence and global optima achievement. In the first 4 test functions all methods have found the global minima but COA has converged faster in less iterations. But in the last test function (10- dimensional Rastrigin function) GA and PSO could not converge to even a close value of global optima. But COA had found a very good and acceptable estimation of global minimum in just 66 iterations. Off course, it should be noted that the higher performance of COA in reaching better results for these 5 benchmark functions and a real case study does not necessarily mean that COA is the ever best evolutionary method developed. It just can be considered as a successful mimicking of nature; suitable for some sort of optimization problems.