STUDY ON HARMONY SEARCH AND CUCKOO SEARCH ALGORITHM

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1 International Journal of Recent Innovation in Engineering and Research Scientific Journal Impact Factor by SJIF e- ISSN: STUDY ON HARMONY SEARCH AND CUCKOO SEARCH ALGORITHM S. Induja 1 and Dr.V.P.Eswaramurthy 2 1 Research Scholar, Periyar University, Salem , Tamilnadu, India 2 Assistant Professor, Department of Computer Science, Government Arts and Science College, Komarapalayam, Tamilnadu, India Abstract- Harmony Search (HS) is a meta-heuristic algorithm, based on the musical improvisation method for its processing. In recent times, HS is becoming a preferred algorithm in the evolutionary computation field due to its own priority to many other algorithms. Cuckoo search algorithm is used in solving optimization problems. More over, this algorithm increases the optimization rate, convergence and efficiency. This paper thoroughly reviews and analyzes the main characteristics and application portfolio of the so-called Harmony Search algorithm and Cuckoo search algorithm. Keywords - HS, CS, Optimal solution, DP I. INTRODUCTION The utilization optimization algorithms to reality issues has obtained momentum in the last 10 years. Going out back to the early 1940s, customized mathematical methods such as linear programming (LP), nonlinear programming (NLP) or even dynamic programming (DP) were first sorted out for solving complex optimization issues by switching to various relaxation methods of the primary formulation. These strategies are equipped for cost-efficiently obtaining a worldwide optimal remedy in trouble designs subject to selected particularities, unfortunately their application series does not include the complete course of NP-complete problems, in which an exact approach can never be found in polynomial time. In fact, the remedy space of the problem speeds up substantially with the lots of inputs, making these inconceivable for effective applications. In an effort to cope up with the above shortcoming, meta-heuristic techniques conceived as intelligent self-learning algorithms stemming from the study and mimicking of intelligent processes and behaviours occurring naturally, social problems and other aspects have come into view for effectively tacking this type of hard optimization paradigms. In most cases, meta-heuristics are deemed the most effective choices to seek out and determine a near-optimal solution just one without relying on exact yet computationally challenging algorithms, and by overcoming the major drawback of local search algorithms, ie, getting an stagnant in biased localized areas far from the sought universal solution. Actually, on the last option lays one of the major design issues of modern metaheuristic methods: to avoid local optima to be able to get global optima, which can be achieved by exploring the entire search space through the use of intelligent stochastically driven providers. On this objective, it is of utmost significance for enhancing the overall search performance of the metaheuristic to stabilize the trade off between two essential relevant aspects: diversification and intensification. On one side, diversification represents a form of randomization added to the deterministic elements generating the algorithm procedure in order to explore the finding place in an extensive manner, while intensification represents the improvement of available partial strategies to the problem by discovering the vicinity of such solutions in the region area. If diversification is enough powerful, numerous regions of the search space might be loosely explored, which will reduce the convergence rate of the algorithm. By contrast, if diversification is kept low in the algorithm design, there is certainly an important risk of leaving a part of the solution area unexplored or perhaps generating far-from-optimal solutions owing to trapping in local optima. An appropriate intensification is accomplished by means of exploiting the information attained from past rights Reserved Page 33

2 and by properly fine-tuning the variables that deal with the performance of the algorithm for a particular issue in an effort to improve their convergence characteristics. Cuckoo search algorithm, another form of bio mimicry in the optimization technique, which produces the best reproduction strategy technique of the well known brood parasitic bird, the cuckoos, which has been proposed by Yang and Deb just lately[1]. Cuckoos, most probably one of the vicious and crafty species of all fowl breeds, clandestinely lay their eggs in the nests of other host wild birds, sparing themselves the parental tasks of raising the young. Actually cuckoos practice the art of deceptiveness on a regular basis in their reproductive system. They mimic the color along with pattern of the host eggshell in order to disguise their ova from being detected by the host birds.to make more area and food for their younger chick,cuckoos will steal the host egg whilst sneaking their personal into the nest. however, the connection among the host species and the cuckoos is usually a continuous arms race. The hosts discover ways to discern the imposters and that they either throw out the parasitic eggs or wilderness the nest; the parasites improve the forgery ability to make their eggs to appear greater alike with the host eggs. The feasibility of applying the CSA to find the global most fulfilling for the optimization problems has been investigated within the literature. In the pioneering research work of Yang and Deb[1], the CSA has been applied effectively in optimizing numerous benchmark functions, and their findings confirmed that the worldwide seek capability of the CSA is extra green than GA and PSO [2, 3].Alternatively, the CSA has been employed in numerous domain names considering its inception; consisting of engineering layout technique [4 7], chaotic system [8], wi-fi sensor networks [9,10], structural optimization problem [4, 11, 12], image processing [13, 14], mining technique [20], and scheduling trouble [10 13]. Unquestionably, its recognition increases unceasingly in the not-to-distant future. II. HARMONY SEARCH ALGORITHM The HS algorithm was formerly evolved with the aid of Geem et al. in 2001[24], and is totally based on l musical performance methods, that show up whilst a musician searches for a higher level of harmony, including jazz. Jazz improvisation seeks to discover musically attractive concord (an excellent country) as determined through a cultured preferred, simply because the optimization system seeks to discover a worldwide solution (an excellent nation) determined with the aid of an objective characteristic. The pitch of every musical device determines the aesthetic satisfactory, just because the goal feature value is decided by the set of values assigned to every layout variable [19]. HS algorithm proved to be very a effective one in a huge variety of optimization troubles, including water distribution and games[20 24], and showed higher performance in evaluation of different conventional optimization techniques. The benefits of HS can be summarized as proven beneath [24]: i. HS algorithm uses fewer mathematical techniques and does now not require any initial values for the variables. ii. Since, the HS algorithm uses stochastic random searches, descriptive and derivative information is also not needed. iii. HS algorithm generates a new vector, after thinking about all of the existing vectors, while the genetic set of rules (GA) most effective considers the two parent vectors. these features increase the power of the HS algorithm and convey higher answers. iv. HS is right at figuring out the excessive overall performance regions of the solution area at a reasonable time. In HS algorithm, the Harmony Memory(HM) stores the feasible vectors, that are all in the feasible space. when a musician improvises one pitch, normally one among three guidelines is used: i. Generating anyone pitch from his/her memory, i.e. choosing any one fee from Harmony memory, defined as memory consideration; ii. Generating a nearby pitch of 1 pitch in his/her memory, i.e. deciding on an adjoining cost of one cost from concord memory, defined as pitch modifications; iii. Producing totally a random pitch Available Online at : Page 34

3 from viable sound levels, i.e. deciding on absolutely random price from the feasible fee variety, described as randomization. In addition, whilst every selection variable chooses one value inside the HS algorithm, it could follow one of the abovementioned policies within the whole HS algorithm. If the new harmony vector is better than the worst harmony vector inside the harmony memory, then the new harmony vector will changed to the place of the worst one. This procedure is repeated till a preventing criterion is reached[25-27]. Algorithm 1: Harmony search algorithm. III. APPLICATIONS OF HARMONY SEARCH ALGORITHM A) IN THE FIELD OF COMPUTER SCIENCE The usage of HS algorithm has been applied recently in many fields in computer science and engineering, including the clustering or grouping of web pages, the text summarization, Robotics, Internet routing, etc. HS has been applied successfully to the issue of clustering of web pages, for both continuous data representation [30] and as well as for discrete data representation [29]. For clustering, hybrid HS with the k-means algorithm, HSCLUST resulting in [31]. It depicts that clustering web documents based on HS ia a better option when we deal with larger number of documents [28] HS was utilized for reducing the period of motion, which is really the main restriction of issues which may have mainly directed at increasing performance, for robots used in the industrial field. HS was hybridized with the sequential quadratic encoding (SQP, Sequential acronyms Quadratic Programming), ending HHSA to improve the solutions measured trajectories, using randomness in order to find the optimal introductory ideals which are to be utilized for SQP vectors. B) IN THE FIELD OF ECOLOGY In today s modern life, urban conservation ecosystems and their species are very important. To achieve this, different techniques have been developed and used for the selection problem. In the field of optimization,various classes of reserve selection problems have been discovered: species recovery problem (Species September Covering Problem, SSCP) recovery problem Maximum species (Maximal Covering Species Problem, MCSP), maximum representation problem of multiple species (Maximal Multiple-Species Representation Problem, MMRSP) problem restricting opportunity preservation (chance constrained covering problem) and problem preservation of expected (expected covering problem). Of the above optimization problems, MCSP has been known to be the most commonly used model [32, 33, 34]. HS algorithm has been modified to adapt to various features of the problem such as: limited selection (sparse selection), selection of the first major book (big-book-first selection) and selection of the first diversity (diversity-first selection). C) IN THE FIELD OF CIVIL ENGINEERING Minimizing prices and area materials, area unit a number of the foremost vital options within the style, development and implementation of a structure or building. HS has undergone multiple Available Online at : Page 35

4 connected applications construction, network style and optimisation of resources, wonderful results compared to different algorithms within the minimizing the full price of construction (production), that for engineering science is that the main issue and each company appearance, going aside the standard of materials and construction itself. once speech structural style refers to the structure or frame construction, that is, the beams and columns steel, that support the load of the development [35-37]. D) IN THE FIELD OF ECONOMICS A classic and necessary downside in economic science is that the total radii. The total of radii is an element of the half programming, that aims at optimizing one or a lot of radios of assorted functions. This downside thought of terribly troublesome as a result of it involves nonconvex and/or multimodal functions. Among the applications of the issues total of radii are: government catching, the transportation science, finance, economics, engineering [20]. moreover, in their analysis half, these issues have theoretical and procedure challenges.[38]. IV. CUCKOO SEARCH ALGORITHM The Cuckoo Search Algorithm, which is evolved from inspiration of cuckoo s adaption to breeding and reproduction, is summarized as follows: (i) Each cuckoo lays one egg, randomly in a selected host nest at a time, where the egg denotes the possible solution for the problem under study; (ii) The CSA follow up the survival of the fittest principle. Only the fittest among all the host nests with high quality eggs will be stepped out to the next generation; (iii) The number of host nests in the CSA is fixed. The host bird identify the intruder egg with a probability pa [0, 1]. For such kind of incidents, the host bird will either evict the parasitic egg or abandon the nest totally and seek for a new site to rebuild the nest. begin Generate initial population of q host nest xi, i = 1, 2,..., q for all xi do Evaluate the fitness function Fi = (xi) end for while (iter < MaxGeneration) or (stopping criterion) Generate a cuckoo egg xj from random host nest by using L evy flight Calculate the fitness function Fj = (xj) Get a random nest i among q host nest if (Fj > Fi) then Replace xi with xj Replace Fi with Fj end if Abandon a fraction pa of the worst nests Build new nests randomly to replace nests lost Evaluate the fitness of new nests end while end Algorithm 2: Cuckoo search algorithm. Available Online at : Page 36

5 V. FLOWCHART OF CUCKOO SEARCH Figure 1: Flowchart of Cuckoo search algorithm. VI.APPLICATIONS OF CUCKOO SEARCH Cuckoo Search algorithm are used for solving A)Financial Demands Distribute issues. B) Expense and Vulnerabilities optimization challenges in Cloud computing are dealed by using CS algorithm with Levy flights. C) For Optimum Network Reconfiguration and Distributed generation adaptive Cuckoo search algorithm can be used. D) Cuckoo search algorithm for feature selection. E) For Secured Vehicular Adhoc Network (VANET), cuckoo search algorithm can be enforced. VII.CONCLUSION AND FUTURE WORK Harmony search algorithm has proved to be a powerful tool for solving numerous optimization issues. It may not need any specific mathematical calculations to obtain the optimum solutions. In recent times, HS was applied to a lot of optimization issues, demonstrating its efficiency compared to other heuristic algorithms and other Meta mathematical optimization strategies. Continuous development improvements to the algorithm and various applications to new varieties of problems (operations research, economy, computer science, civil engineering and electrical engineering), indicate that HS is an excellent option. As a consequence, a lot of reports have been presented to improve their functionality. However, there are a lot future works that can be done, like: Investigation of the way to prevent getting fixed in local solution, because most of the proposed HS had a problem of being, so. Implement HS on dynamic issues. Improve and analyze ensemble of HS operators algorithms. CS is the most effective search algorithm that it motivated by the breeding behaviour of cuckoos. It gives the brief description of the applications of the nature-inspired algorithm. CS algorithm is within a variety of fields including Industry, Image processing, wireless sensor networks, flood forecasting, document clustering, speaker recognition, shortest path in distributed system, in the health sector, job scheduling. The Cuckoo algorithm performs various nature-inspired algorithms with regards to upgraded performance and also less computational period REFERENCES [1] Xin She Yang and Sush Deb, "Nature & Biologically Inspired Computing," in IEEE, University of Cambridge, Trumpinton Street, CB2 1 PZ, UK, Available Online at : Page 37

6 [2] X.-S. Yang and S. Deb, Cuckoo search via L evy flights, in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC 09), pp , December [3] X.-S. Yang and S. Deb, Engineering optimisation by cuckoo search, International Journal of Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp , [4] I. Durgun and A. R. Yildiz, Structural design optimization of vehicle components using Cuckoo search algorithm, Materials Testing, vol. 54, no. 3, pp , [5] J. Piechocki, D. Ambroziak, A. Palkowski, and G. Redlarski, Use of Modified Cuckoo Search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms, Applied Energy, vol. 114, pp , [6] A. H. Gandomi, S. Talatahari, X.-S. Yang, and S. Deb, Design optimization of truss structures using cuckoo search algorithm, Structural Design of Tall and Special Buildings, vol. 22, pp , [7] R. Sharma, K. P. S. Rana, and V. Kumar, Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator, Expert SystemswithApplications, vol. 41,no. 9, pp , [8] X.-T. Li and M.-H. Yin, Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method, Chinese Physics B, vol. 21, no. 5, Article ID , [9] M. Dhivya and M. Sundarambal, Cuckoo Search for data gathering in wireless sensor networks, International Journal of Mobile Communications, vol. 9, no. 6, pp , [10] S. Goyal and M. S. Patterh, Wireless sensor network localization based on cuckoo search algorithm, Wireless Personal Communications, [11] A. Gandomi, X.-S. Yang, and A. H. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Engineering with Computers, vol. 29, no. 1, pp , [12] A. H. Gandomi, X.-S. Yang, and A. H. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Engineering with Computers, vol. 29, no. 1, pp , [13] A.K.Bhandari, V. K. Singh, A. Kumar, andg.k.singh, Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur s entropy, Expert Systems with Applications, vol. 41, pp , [14] A. K. Bhandari, V. Soni, A. Kumar, and G. K. Singh, Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD, ISA Transactions, vol. 53, no. 4, pp , [15] A. R. Yildiz, Cuckoo search algorithmfor the selection of optimalmachining parameters inmilling operations, International Journal of AdvancedManufacturing Technology, vol. 64, no. 1 4, pp , [16] K. Chandrasekaran and S. P. Simon, Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm, Swarm and Evolutionary Computation, vol. 5, pp. 1 16, [17] S. Burnwal and S. Deb, Scheduling optimization of flexible manufacturing system using cuckoo search-based approach, The International Journal of Advanced Manufacturing Technology, vol. 64, no. 5-8, pp , [18] M. K. Marichelvam, T. Prabaharan, and X. S. Yang, Improved cuckoo search algorithmfor hybrid flow shop scheduling problems to minimize makespan, Applied Soft Computing Journal, vol. 19, pp , [19] Lee, K.S., Geem, Z.W., Lee, S.-H. and Bae, K.-W. The harmony search heuristic algorithm for discrete structural optimization", 2005, Engineering Optimization, 37(7): [20] Yang, X.-S., Harmony Search as a Metaheuristic Algorithm, in Music-Inspired Harmony Search Algorithm. 2009, Springer Berlin / Heidelberg. p [21] Geem, Z.W., Optimal Design of Water Distribution Networks Using Harmony Search. 2009: p [22] Geem, Z.W., Optimal cost design of water distribution networks using harmony search. Engineering Optimization, [23] Geem, Z.W., Harmony search algorithms for structural design optimization, in Studies in computational intelligence, v , Springer Berlin Heidelberg: Berlin, Heidelberg. p [24] Geem, Z.W., Harmony search algorithm for solving Sudoku, in Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I. 2007, Springer-Verlag: Vietri sul Mare, Italy. [25] Geem, Z., J. Kim, and G.V. Loganathan, A New Heuristic Optimization Algorithm: Harmony Search. Simulation, (2): p [26] Lee, K. and Z. Geem, A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Computer Methods in Applied Mechanics and Engineering, (36-38): p [27] Geem, Z.W., Recent Advances In Harmony Search Algorithm. Studies in Computational Intelligence. Vol , Annandale,Virginia: Springer [28] Forsati, R. and M. Mahdavi, Web Text Mining Using Harmony Search, in Recent Advances In Harmony Search Algorithm. 2010, Springer Berlin / Heidelberg. p [29] Mahdavi, M. and H. Abolhassani, Harmony K-means algorithm for document clustering. Data Mining and Knowledge Discovery, (3): p [30] Mahdavi, M., et al., Novel meta-heuristic algorithms for clustering web documents. Applied Mathematics and Computation, (1-2): p Available Online at : Page 38

7 [31] Forsati, R., et al. Hybridization of K-Means and Harmony Search Methods for Web Page Clustering. In Web Intelligence and Intelligent Agent Technology, 2008.WI-IAT 08. IEEE/WIC/ACM International Conference on [32] Geem, Z., C.-L. Tseng, and J. Williams, Harmony Search Algorithms for Water and Environmental Systems, in Music-Inspired Harmony Search Algorithm. 2009, Springer Berlin / Heidelberg. p [33] Cheng, Y.M., et al., An improved harmony search minimization algorithm using different slip surface generation methods for slope stability analysis. EngineeringOptimization, (2): p [34] Fesanghary, M., E. Damangir, and I. Soleimani, Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm. Applied Thermal Engineering, (5-6): p [35] Saka, M.P., Optimum design of steel sway frames to BS5950 using harmony search algorithm. Journal of Constructional Steel Research, (1): p [36] Geem, Z. and S. Degertekin, Optimum Design of Steel Frames via Harmony Search Algorithm, in Harmony Search Algorithms for Structural Design Optimization. 2009, Springer Berlin / Heidelberg. p International Journal of Computer Applications ( ) Volume 70 No.28, May 2013 [37] Saka, M. and F. Erdal, Harmony search based algorithm for the optimum design of grillage systems to LRFD-AISC. Structural and Multidisciplinary Optimization, (1):p [38] Jaberipour, M. and E. Khorram, Solving the sum-ofratios problems by a harmony search algorithm. Journal of Computational and Applied Mathematics, (3):p Available Online at : Page 39