Research Cell: An International Journal of Engineering Sciences

Satyendra Nath Mandal ¹, Arghya Ghosh ², Subhojit Roy ², J. Pal Choudhury ¹, S. R. Bhadra Chaudhuri ³

Affiliations
1 Dept. of I.T, Kalyani Govt. Engg College, Kalyani, Nadia(W.B), IN
2 Dept. of CSE, Kalyani Govt. Engg College, Kalyani, Nadia(W.B), IN
3 Dept. of ETC, BESU, Howrah ( W. B), IN

Abstract

The productivity of plant can be measured in terms of pod yields produce by that plant. The production of plant dependent on various parameters of the plant like shoot length, number of leaves, ischolar_main length , ischolar_main numbers etc. Some other factors like soil, crop and distance management are also taken care of to produce maximum amount of yield. It is not desirable to use the no of leaves of the tree to calculate the growth of the plant because when the plant is growing some leaves may be lost and some new leaves may appear. It is also very difficult to measure the number of ischolar_mains and length of growth of ischolar_main in several time instances as it grows underground. So, it is very convenient to measure the plant growth on the basis of shoot length. In this paper, an effort has been made to predict the shoot length of mustard plant by Tabu Search (TS). The average error has been calculated based on the actual shoot and predicted shoot length. A comparitive study has been made among the different methods applied on same data set and one method has been selected based on minimum average error. The shoot length at maturity has been predicted by applying least square method on predicted data set with minimum average error. Finally, pod yield at maturity has been predicted by shoot length at maturity.

Keywords

Tabu Search, Soft Computing, Prediction, Forecasting, Average Error, Pod Yield.

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Satyendra Nath Mandal ¹, Soumi Sadhu ²

Affiliations
1 Dept. of I.T, Kalyani Govt. Engg College, Kalyani, Nadia(W.B), IN
2 Dept. of CSE, Kalyani Govt. Engg College, Kalyani, Nadia(W.B), IN

Abstract

Harmony Search Algorithm originally came from the analogy between the music improvisation and optimization process. The harmony search algorithm is searching the optimal solution using the novel stochastic derivative equation. The equation is based on density information of multiple solution vectors, instead of calculus based gradient information of single solution vector. In other words, if a certain value frequently appears in multiple vectors, then the value has higher chance to be selected rather than the other candidate values. In this paper, an algorithm has been proposed to solve the Sudoku problem based on harmony search. A comparative study has been made with the proposed algorithm with the existing solution of Sudoku problem by harmony search. It has been proved that the proposed algorithm minimized the iterations to solve the Sudoku problem.

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