Efficient Economic Profit Maximization: Genetic Algorithm Based Approach

Economic profit is the main governing power of any industrial and socio-economic growth. It is imperative to maximize profit related with economic systems to retain economic stability. Traditional methods involving Lewis model has been found to be unsuitable in terms of computational complexity. Motivated by the recent developments and successful application of meta-heuristic algorithms in achieving potent solutions, the present work proposed efficient meta-heuristic algorithm to support the profit maximization formalism. The genetic algorithm (GA) is employed to maximize the profit in terms of the total revenue (TR) and total cost (TC). The real parameter objective function depicting profit has been gradually optimized by GA. Experimental results suggest that the GA based profit maximization method is extremely fast, accurate and robust.

[1]  Yaduvir Singh,et al.  Genetic Algorithms: Concepts, Design for Optimization of Process Controllers , 2011, Comput. Inf. Sci..

[2]  Nilanjan Dey,et al.  MEDLINE Text Mining: An Enhancement Genetic Algorithm Based Approach for Document Clustering , 2016, Applications of Intelligent Optimization in Biology and Medicine.

[3]  Nilanjan Dey,et al.  Optimization of 5.5-GHz CMOS LNA parameters using firefly algorithm , 2017, Neural Computing and Applications.

[4]  Nilanjan Dey,et al.  Optimized Tumor Breast Cancer Classification Using Combining Random Subspace and Static Classifiers Selection Paradigms , 2016, Applications of Intelligent Optimization in Biology and Medicine.

[5]  Nilanjan Dey,et al.  Firefly algorithm for optimized nonrigid demons registration , 2016 .

[6]  Nilanjan Dey,et al.  Parameter Optimization for Local Polynomial Approximation based Intersection Confidence Interval Filter Using Genetic Algorithm: An Application for Brain MRI Image De-Noising , 2015, J. Imaging.

[7]  Malcolm Irving,et al.  Economic dispatch of generators with prohibited operating zones: a genetic algorithm approach , 1996 .

[8]  Cars Hommes,et al.  Genetic algorithm learning in a New Keynesian macroeconomic setup , 2017, Journal of Evolutionary Economics.

[9]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[10]  Nilanjan Dey,et al.  Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search , 2013, Int. J. Bio Inspired Comput..

[11]  Nilanjan Dey,et al.  Forest Type Classification: A Hybrid NN-GA Model Based Approach , 2016 .

[12]  Ja-Chen Lin,et al.  Image hiding by optimal LSB substitution and genetic algorithm , 2001, Pattern Recognit..

[13]  Nilanjan Dey,et al.  Firefly Algorithm for Optimization of Scaling Factors During Embedding of Manifold Medical Information: An Application in Ophthalmology Imaging , 2014 .

[14]  Ali Azadeh,et al.  A new genetic algorithm approach for optimizing bidding strategy viewpoint of profit maximization of a generation company , 2012, Expert Syst. Appl..

[15]  Kyung-shik Shin,et al.  A genetic algorithm application in bankruptcy prediction modeling , 2002, Expert Syst. Appl..

[16]  Jasmina Arifovic.  Genetic algorithm learning and the cobweb model , 1994 .

[17]  Nilanjan Dey,et al.  Systematic Analysis of Applied Data Mining Based Optimization Algorithms in Clinical Attribute Extraction and Classification for Diagnosis of Cardiac Patients , 2016, Applications of Intelligent Optimization in Biology and Medicine.

[18]  G. Sheblé,et al.  Genetic algorithm solution of economic dispatch with valve point loading , 1993 .

[19]  Thomas Riechmann.  Genetic Algorithms and Economic Evolution , 1998 .

[20]  Nilanjan Dey,et al.  Indian Sign Language Recognition Using Optimized Neural Networks , 2015, ITITS.

[21]  Sylvie Geisendorf,et al.  Genetic Algorithms in Resource Economic Models , 1999 .

[22]  J. Beasley,et al.  A genetic algorithm for the set covering problem , 1996 .

[23]  Colin R. Reeves,et al.  A genetic algorithm for flowshop sequencing , 1995, Comput. Oper. Res..