Finding the best answer among the various solutions to complex and mathematical problems is called optimization. There are two types of optimization problems; continuous optimization and discrete optimization. Finding the solution in these environments is the best solution for that particular solution. Optimization exists in many fields and sciences, and it shows that if researchers provide the most quality optimization algorithms, it can have a great impact on human life. Optimization is similar to finding a treasure in an area. In this analogy, you have to mobilize a crowd to find this treasure. Since the population does not know the location of the treasure from the beginning, these populations will start searching at random and will reach near to it at a certain time. The topic of the search here is very important. It is very important to find a mechanism that can best organize the population. The search engine must follow certain ideas and rules. In the optimization problem, the most important step is proper search. In optimization issues, the concept of the best answer, best search, best solution and best organization is desired. Nowadays, optimization can be applied everywhere we deal with big data.

New algorithms have been developed to see if they can cope with these challenging optimization problems. Among these new algorithms, many algorithms, such as particle swarm optimization, cuckoo search, and firefly algorithm, have gained popularity due to their high efficiency. In the current literature, there are about 40 different algorithms. It is a challenging task to classify these algorithms systematically. In this chapter, the reader becomes familiar with the source of nature so that he can come up with an idea. Therefore, the first step in building and delivering a natureinspired algorithm is to become familiar with nature and understand its features. Nature is a great source of inspiration for all stages of human life. In nature, creatures and structures always find solutions to their problems. Hence, it is nature that plays the leading role. Nature-inspired optimization algorithms are always some of the best mechanisms to solve complex problems. In this chapter, the reader will be introduced to a variety of nature-based optimization algorithms. Optimization algorithms are introduced and their techniques will be examined. This chapter has a history of natureinspired algorithms whose evolution is visible. Researchers have tried to draw inspiration from natural resources as well as animals from nature that provided algorithms that have helped researchers in many problems. This chapter can also introduce readers to the history of making nature-based algorithms.

This chapter introduces examples of nature-inspired algorithms presented by authors in recent years. These algorithms all use the source of nature, and the nature and behavior of some animals are the main basis of these algorithms. These algorithms show the orderly behavior of some natural animals and show how this targeted order becomes an algorithm. Understanding these algorithms can help the reader understand how to transform the idea of nature into meaningful equations. We present some examples of these algorithms in this chapter to familiarize the reader with the order in some natural animals. Also, in this chapter, we can understand how to transform this natural order into meaningful equations. These meaningful equations are introduced in the form of an optimization algorithm. In this chapter, the algorithm SSPCO that inspired by the behavior of See-see partridge chickens, SSPCO algorithm based on chaotic population, data clustering using algorithm SSPCO algorithm, data clustering with algorithm chaotic SSPCO, Solving the Travelling salesman problem with the help of SSPCO algorithm, escape from hunter particle swarm optimization algorithm and birds algorithm based on classical condition learning, provided. In this chapter, we are going to introduce the reader to a number of algorithms presented and published by the author of the book. We are going to understand how an idea becomes a mathematical formula. Articles are available in the magazines which can be referred to for additional details.

In recent years, recognizing amazing resources in nature can be a way to formulate ideas for optimization problems. First, the ideas are selected in nature, and then the hidden purposeful behavior of these ideas is discovered and expressed as a systematic algorithm. Choosing and observing the order in animals and nature is an art, and researching them is a practical way of analyzing them. The most important part is that these behaviors must be selected in order and then formulated mathematically. This chapter will discuss some techniques for converting ideas into algorithms and a specific framework. Some of the important principles in converting behaviors in nature into mathematical equations are outlined in this chapter If one can find the best and easiest way to transform an idea into a mathematical equation in the form of an algorithm, then one can claim that an efficient algorithm is presented that can solve a complex problem. If some of the principles outlined in this chapter are followed, a good algorithm can be derived from a natural idea. This chapter introduces examples of nature-inspired algorithms presented by authors in recent years. These algorithms all use the source of nature, and the nature and behavior of some animals are the main basis of these algorithms. These algorithms show the orderly behavior of some natural animals and also show how this targeted order can be transformed into an algorithm. Understanding these algorithms can help the reader understand how to transform the idea of nature into meaningful equations. We present some examples of these algorithms in this chapter to familiarize the reader with the order in some natural animals. Also, in this chapter, we can understand how to transform this natural order into meaningful equations. These meaningful equations are introduced in the form of an optimization algorithm.

Nature-inspired algorithm is types of computing systems use a variety of phenomena in nature to create a coherent mechanism. Designing different systems and creating learning machines as well as optimization are some of the factors that have chosen nature. These systems come from nature and have designed interesting mechanisms. The nature of the search problem is very important in nature and the species of animals and even the natural structures each have a kind of search system inherently. In this book, optimization and optimization algorithms are examined, and solutions are proposed to create a nature-inspired optimization algorithm, and even suggestions are made for natural phenomena that can be transformed into algorithms. The sciences, industry, medicine, and other fields can find algorithms that fit their field by reading this book. Collective intelligence is one of the main phenomena found in nature, and this book also emphasized this. This book first describes optimization, then defines the optimization problem and describes its mechanism. Then nature-inspired optimization algorithms were evaluated and a number of them were introduced. The source of nature was then discussed and explained why nature is a good source of ideas for building an algorithm. A number of authors' algorithms were studied to familiarize the reader with these types of algorithms and then ideas of nature were proposed to the reader. Finally, how to convert an idea into an algorithm is discussed.