Literature Review: The Theory Of Planned Behavior - congratulate
Human Relations Theory Uncategorized The theory paper assignment is an analysis of a theory of human behavior utilized in understanding the lifespan. The focus of the course is on understanding human behavior and social environment; therefore, practice issues e. Instructions Headings and subheadings are required. The paper must be at least four 4 full pages and no more than five 5 pages in length. The cover and reference pages are not included in the page count. Who are the major theorist s for each theory? Literature Review: The Theory Of Planned BehaviorPublished16 Apr Abstract Harmony search algorithm is the recently developed metaheuristic in the last decade. It mimics the behavior of a musician producing a perfect harmony. It has been used to solve the wide variety of real-life optimization problems due to its easy implementation over other metaheuristics. It has an ability to provide the balance between exploration and exploitation during search. In this paper, a systematic go here on harmony Bhavior algorithm HSA is presented. The natural inspiration and conceptual framework of HSA are discussed. The control parameters of HSA are Literature Review: The Theory Of Planned Behavior with their mathematical foundation. The improvement and hybridization in HSA with other metaheuristics are discussed in detail.
The applicability of HSA in different problem domains is studied. The future research directions of HSA are also investigated. Introduction Optimization is the process for selecting the best possible value of decision variables from the available set of values under some specific constraints [ 1 ]. This process is used to minimize or maximize the fitness function of the optimization problems. The fitness function is influenced by a set of variables, called decision variables and a Literxture of restrictions, which acts as a barrier to certain values of the decision variables. Based on the nature of search space, the optimization problems are broadly categorized into two main classes such as continuous and discrete [ 2 ].
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Beside this, the categorization of problems based on nature of fitness function is linear, convex, quadratic, concave, and sparse. The nature of problem has to be analyzed before applying the optimization techniques [ 3 ]. A large number of optimization techniques are available in the literature to solve the real-life problems. The classical techniques such as quasi-Newton method, dynamic programming, and linear programming are used for solving the real-life problems. Along with the evolution of new techniques, the complexities of the optimization problems in engineer designs affronted were increased. These methods are unable to solve these problems [ 3 ]. Due to the complexities of problems, it becomes difficult to solve it by using the classical techniques in the reasonable time duration.
To alleviate this issue, metaheuristic techniques are developed in the recent years. These techniques are able to solve the complex problems in a reasonable time period. Metaheuristic techniques are inspired from the ideas derived from biological science, physics, behavior of animals, insects, etc. A number of metaheuristic algorithms are developed in the literature. Figure 1 Classification of metaheuristic techniques. Harmony search algorithm HSA is a well-known metaheuristic link, which is developed by Geem et al.
HSA is inspired from the improvisation process of musicians.
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A musician searches for the perfect notes to develop a perfect harmony. Based on this concept, HSA was developed to search a good solution for an optimization problem. The developers of HSA performed extensive experimentation to validate its performance.
The main characteristics of Thery are as follows: 1 the initial setting of decision variables is not required, 2 derivate information is not required, and 3 few control parameters are needed for fine tuning [ 6 ]. Due to these characteristics, HSA is preferred over the other existing metaheuristic techniques. HSA is used to solve the wide variety of problems such as clustering problems, social-economic problems, power engineering problems, computer vision problems, scheduling problems, and global optimization problems.]
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