How to Design Optimization Algorithms by Applying Natural Behavioral Patterns E-Book
Rohollah Omidvar
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- Herausgeber: Bentham Science Publishers
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
How to Design Optimization Algorithms by Applying Natural Behavioral Patterns is a guide book that introduces readers to optimization algorithms based on natural language processing. Readers will learn about the basic concept of optimization, optimization algorithm fundamentals and the methods employed to formulate natural ideas and behaviors into algorithms. Readers will learn how to create their own algorithm from the information provided in the text.
The book is a simple reference for students and programming enthusiasts who are interested in learning about optimization and the process of designing algorithms designed to mimic natural phenomena. The information will also benefit engineers who are learning about optimization techniques.
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Seitenzahl: 173
Veröffentlichungsjahr: 2021
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FOREWORD
Collective intelligence means that instead of using one thought, one can use multiple sets of thoughts, and collective intelligence in nature has greatly helped its survival. The meta-heuristic algorithms make use of all sources and provide a structure based on natural phenomena. Hence, the source that the algorithms use can determine their type and determine their quality. While there are some algorithms that are not suitable, most of the algorithms that used the natural resource were able to create a suitable framework under an optimization algorithm.
PREFACE
Most of the books presented by respected researchers in the field of optimization have introduced algorithms. So I thought young researchers and students needed a reference to research in this area and create their own algorithm. Nature has always been full of secrets in the history of mankind, and from time to time, human beings discover some of these mysteries. But there is still much to be desired until the day that man reaches the secrets of nature. With these discoveries, he has been able to propose important mechanisms based on these mysteries of nature. In this book, I talk mostly to young people and students who are interested in science. I try to share with them the little science I have in this area.
DEDICATION
I dedicate this book to the soul of Ms. Narjes Khanalizadeh. She was the first Iranian nurse to die of coronavirus. Narjes was born in 1995 in the city of Rudsar in northern Iran. After graduation, she worked as a nurse at the emergency department of Lahijan city. Narjes Khanalizadeh, on February 23, 2020, after the widespread outbreak of coronavirus in Iran, with similar complications of the coronavirus, lost consciousness and fell to the ground while taking care of patients at work. Narjes Khanalizadeh died in the afternoon of February 25, 2020 at Milad Hospital in Lahijan.
Introduction to Optimization
Rohollah Omidvar,Behrouz Minaei BidgoliAbstract
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.
Optimization problems are called complex and mathematical problems that are more complex than other problems. There are two types of continuous and discrete variables in optimization problems. In a discrete optimization problem, we are looking for an object such as an integer, permutation or graph from a countable set. Problems with continuous variables include constrained problems and multimodal problems. It can be said that optimization is a kind of mathematical programming. As such, optimization has been able to solve many problems in the sciences, including physics, biology, engineering, economics and business. In most cases there is some kind of mathematical problem that needs to be solved It Algorithms convert and solve these problems into mathematical problems.
The historic term mathematical programming, broadly synonymous with optimization, was coined in the 1940s before programming became equated with computer programming. Mathematical programming includes the study of the mathematical structure of optimization problems, the invention of methods for
solving these problems, the study of the mathematical properties of these methods, and the implementation of these methods on computers. Faster computers have greatly expanded the size and complexity of optimization problems that can be solved. The development of optimization techniques has paralleled advances not only in computer science but also in operations research, numerical analysis, game theory, mathematical economics, control theory, and combinatory.
Optimization must have to consider three main problems. The first problem is that the answer is not exactly clear what. Whether the answer is a minimum number or a range of company costs, this type of answer must be precisely specified. The second problem is that sometimes it is necessary to manipulate values rather than an answer. Examples include quantities of stock to buy or sell the amount of different resources that must be allocated to different production activities, the route followed by the vehicle through the traffic network, or the policies that must be supported by a candidate. The final problem with optimization is that the answer area or the space of the item must be specified. For example, a production process may not require more resources than the available resources and it may not use less than zero resources. In this broad framework, optimization problems can have different mathematical properties. Mathematical optimization or mathematical programming is the selection of a best element (with regard to some criterion) from some set of available alternatives [1]. Optimization problems from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics [2].
In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. More generally, optimization includes finding “best available” values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.
During the optimization, the initial algorithms are studied by the different methods and the obtained information is used to improve a thought or method. An optimization is a mathematical tool, which is concerns with finding the answers to many questions about the quality of solutions of different problems. The term of “the best” implicitly suggests that there are more than one solution to a given problem, which of course the solutions don’t have the identical values. The definition of the best solution depends on the discussed problem as well as the allowable error value. So, the way that the problem is formulated in which, also has a direct impact on the quality of the best solution. Some problems have a clear response; such as the best player of a sport branch, the longest day of the year and solution of an ordinary first grade differential equation are examples that can be named as easy problems. In contrast, some problems have the various maximum or minimum solutions known as the optimal or extreme points and probably would be the best answer to a relative concept. The best work of art, the most beautiful landscape and the most dulcet track of music are among examples that can be said for these problems. Optimization is changing the inputs and characteristics of a device, a mathematical process or an experimental test in a way that the best output or result achieved. Inputs are the variables of a process that are referred to as the objective function, the cost function, or the fitness function.
Optimization is a process followed to improve something. A thought, idea or plan raised by a scientist or an engineer may get better through optimization procedure. During optimization, the initial conditions are investigated through different methods, and the obtained information is used to improve a thought or the used methods. Optimization is a mathematical tool used to find answers of many questions on how various solutions to problems are used. Optimization deals with finding the best response to a problem. The word “best” implies that there is more than one response to a problem but they are of different values. The definition of the “best” response depends on the problem, the solution and the amount of the permissible error. Therefore, the formulation also affects the definition of the best answer directly. Some problems have certain responses; the best player in a sport, the longest day of the year and the answer to an ordinary differential equation of first grade are some examples of simple problems. In contrast, some problems have several maximum or minimum answers known as optimal points or Extremum, and are probably the best answer to a relative concept. The best work of art, the most beautiful landscapes and the most pleasant piece of music can be named as examples of such problems, and Swarm Intelligence is a type of artificial intelligence technique established on the basis of collective behaviour in decentralized and self-organized systems. These systems have populations that are purposefully and socially connected to one another and generate search. Usually these populations are automatically connected to each other and do not require special management. This kind of self-adaptive movement of populations makes the implementation mechanism in different systems easier.
Optimization refers to changing the input and characteristics of a device, or a mathematical process or an empirical test so that the best output or result would be achieved. The inputs are the variables of the process or function, namely objective function, cost function or fitness function. The output is defined as cost, benefit or fitness. In this book, according to many articles related to the topic, all optimization problems have been considered as minimization of a cost function. We can easily show that any optimization problem can be defined as a minimization problem.
Swarm algorithms have a weakness for creating the random initial population. Also, particle swarm algorithm does not consider the quality of the problem space when the particles move in the space, and does not adjust the speed of the particles with that quality. Furthermore, choosing a point between the local and public optimums, the swarm algorithm makes the particles to spend a lot of time to reach the optimal solution.
Optimization always helps human life and makes the system from one state to a better one. The problem of optimization has existed in human minds for a long time. Optimization helps bring the system and the person closer to their goal in life [3]. In fact, in any problem, there is an objective function that must be optimized. Assume that if the optimization point is found, the problem is solved. A goal function can be in the form of fitness or performance. If we are to minimize objective performance, that process can be a cost function. If we want to maximize the performance of a goal, it can be considered as a fitness performance. The problem of optimization has been and is widely affecting all aspects of human life.
Optimization is everywhere, in various fields of production, management, business, military, and decision [4]. While in linear optimization, mathematical linear programming is the best option, it covers a few real problems; i.e. there are a few problems that can be expressed in the form of a linear objective function. In convex optimization, mathematical convex programming is the best option. In spite of limitation of linear objective function, the convex objective function covered much more real problems. Although there are many problems in the science that can be expressed in the form of convex objective function, almost all real problems can't be expressed in terms of convex objective function and they are a non-linear objective function. In engineering, the best product, and manufacturing parameters are considered. In the industry of selecting the right raw materials, its quantity, time and ultimately product output have always been a priority. In economics, resource allocation can best help create a quality economy [5]. Much of human discovery has been inspired by nature and has been in various forms throughout history. For example, they get help from a random mechanism. In human exploration, auxiliary parameters have been used that are often used by nature, and these parameters have been effective in the quality of the explored product [6]. Optimization is actually finding the best situation. The problem of optimization involves many areas in human life. There are also various solutions to finding human issues that have broadened this optimization problem. The algorithms that solve the optimization problem can be definitive or probabilistic. Prior to the optimization algorithms, the solutions were not very suitable and led to failure. For this reason, human beings have increasingly resorted to optimization algorithms and expanded them every day to achieve definitive calculations [7]. Science has shown that the movement of living beings is dome based on a coherent social philosophy that accuracy in these behaviours can be useful to achieve a solution to many complex problems. In Fig. (1), you can see some optimization resources in nature.
Fig. (1)) Algorithms in nature [1].Nature Inspired Algorithms is a very active research area. This is because problems with which we are normally familiar are getting more and more complex due to size and other aspects, but all optimization problems include two types of 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, showing how effective optimization can be in human life if one can find the best optimization algorithms. In optimization, most of the new problems that the existing methods do not work on are resolved. For this reason, it seems that these days and in the future they will be more inspired by nature. To answer the question whether recent algorithms inspired by Nature such as Colony Bee Colony algorithm, Firefly algorithm and Spider Social algorithm, Bet algorithm, Strawberry algorithm, Plant propagation algorithm, Plant propagation algorithm or many have not been effective? These are much more effective in most search/optimization problems than in nature-based algorithms such as genetic algorithms, annealing simulations, anti-cloning and super-optimization. Computer and Biology has a long history. In recent years, computer scientists have been using biology to build advanced computing mechanisms, such as neural networks. Computer science and biology have long been integrated to create computational mechanisms and have yielded good results. In Fig. (2), you can see an example of a problem space in optimization.
Fig. (2)) A sample of optimization problem.
