Hybrid optimization techniques of pattern search and genetic algorithm: A case study in production system

2013 ◽  
Vol 10 (4) ◽  
pp. 179-190 ◽  
Author(s):  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Production System ◽  
Pattern Search ◽  
Optimization Techniques ◽  
Hybrid Optimization ◽  
Hybrid Optimization Techniques

Hybrid Tabu Search Hopfield Recurrent ANN Fuzzy Technique to the Production Planning Problems

2012 ◽  
Vol 2 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Pandian M. Vasant ◽  
Timothy Ganesan ◽  
Irraivan Elamvazuthi
Keyword(s):  
Tabu Search ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Oil Refinery ◽  
Hybrid Optimization ◽  
Real World Problem ◽  
Prime Objective ◽  
The Moment ◽  
Hybrid Optimization Techniques ◽  
Planning Problems

The fuzzy technology reveals that everything is a matter of degree. At the moment, many industrial production problems are solved by operational research optimization techniques, under the considerations of some real assumptions. In this paper, the authors have several applications of fuzzy linear, non-linear, non-continues and other mathematical programming applications. The prime objective of this paper is to investigate a new application to the literature and to solve the crude oil refinery production problem by using the hybrid optimization techniques of Tabu Search (TS), Hopfield Recurrent Artificial Neural Network (HRANN) and fuzzy approaches. In application, the real world problem of refinery model has been developed and thorough comparative studies have been carried on varies optimization techniques. The final results and findings reveal that, the hybrid optimization technique provides better, robust, efficient, flexible and stable solutions.

scholarly journals Data Analytics and Mathematical Modeling for Simulating the Dynamics of COVID-19 Epidemic—A Case Study of India

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 127
Author(s):  
Himanshu Gupta ◽  
Saurav Kumar ◽  
Drishti Yadav ◽  
Om Prakash Verma ◽  
Tarun Kumar Sharma ◽  
...  
Keyword(s):  
Data Analytics ◽  
Reproduction Number ◽  
Compartment Model ◽  
Pattern Search ◽  
Optimization Techniques ◽  
Indian Context ◽  
Time Of Infection ◽  
The Government ◽  
Reported Data

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment.

Genetic algorithm search for critical slip surface in multiple-wedge stability analysis

1999 ◽  
Vol 36 (2) ◽  
pp. 382-391 ◽  
Author(s):  
Anthony TC Goh
Keyword(s):  
Genetic Algorithm ◽  
Stability Analysis ◽  
Slope Stability ◽  
Factor Of Safety ◽  
Search Strategy ◽  
Slip Surface ◽  
Pattern Search ◽  
Optimization Techniques ◽  
Thin Layers ◽  
Critical Slip Surface

Most procedures for determining the critical slip surface in slope-stability analysis rely on traditional nonlinear optimization techniques. The main shortcoming of these techniques is the uncertainty as to robustness of the algorithms to locate the global minimum factor of safety rather than the local minimum factor of safety for complicated and nonhomogeneous geological subsoil conditions. This paper describes the incorporation of a genetic algorithm methodology for determining the critical slip surface in multiple-wedge stability analysis. This search strategy is becoming increasingly popular in engineering optimization problems because it has been shown in a wide variety of problems to be suitably robust for the search not to become trapped in local optima. Three examples are presented to demonstrate the effectiveness of the genetic algorithm approach. The search strategy was found to be sufficiently robust to handle layered soils with weak, thin layers, and as efficient and accurate as the conventional pattern search method.Key words: critical slip surface, factor of safety, genetic algorithms, optimization, slope stability, wedge analysis.

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