Dipti Singh

Self organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems

In general, complexity of large scale problems increases with rise in dimension.Large scale problems require large population size and computational cost.In this paper SOMAQI has been used to solve large scale problems (dim. 100 to 3000).Only a population size 10 is required to solve all dimensional problems.It can be considered as low computational cost technique.It converges fast as compared to other techniques. Generally the complexity of the large scale optimization problem is considered to increase as the size or dimension of the problem increases and to solve these problems; more efficient and robust algorithms are needed. Several experiments have shown that an increment in dimensions of the problem not only requires an increment in population size but increases the computational cost also. In this paper a Self Organizing Migrating Algorithm with Quadratic Interpolation (SOMAQI) has been extended to solve large scale global optimization problems for dimensions ranging from 100 to 3000 with a constant population size of 10 only. It produces high quality optimal solution with very low computational cost and converges very fast to optimal solution.

A Novel Variant of Self-Organizing Migrating Algorithm for Global Optimization

This paper presents a novel variant SOMAQI of population based optimization technique self organizing migrating algorithm (SOMA). This variant uses the quadratic approximation or interpolation for creating a new solution vector in search space. To validate the efficiency of this algorithm it is tested on 10 benchmark test problems and the obtained results are compared with already published results using the same quadratic approximation. On the basis of comparison it is concluded that the presented algorithm shows better performance in terms of number of population size and function mean best.

Optimization of Livestock Feed by Blend of Linear Programming and SOMGA

This paper discusses a self-organizing migrating genetic algorithm for animal diet formulation. In the first phase of work, linear models are formulated for minimum cost and maximum shelf life feed quality, and the second phase discusses a self-organizing migrating genetic algorithm to find quick solution. It also compares the results by linear programming technique (LP) and SOMGA and provides the successful application of soft computing technique in the area of animal diet formulation.

Self Organizing Migrating Algorithm with Nelder Mead Crossover and Log-Logistic Mutation for Large Scale Optimization

This chapter presents a hybrid variant of self organizing migrating algorithm (NMSOMA-M) for large scale function optimization, which combines the features of Nelder Mead (NM) crossover operator and log-logistic mutation operator. Self organizing migrating algorithm (SOMA) is a population based stochastic search algorithm which is based on the social behavior of group of individuals. The main characteristics of SOMA are that it works with small population size and no new solutions are generated during the search, only the positions of the solutions are changed. Though it has good exploration and exploitation qualities but as the dimension of the problem increases it trap to local optimal solution and may suffer from premature convergence due to lack of diversity mechanism. This chapter combines NM crossover operator and log-logistic mutation operator with SOMA in order to maintain the diversity of population and to avoid the premature convergence. The proposed algorithm has been tested on a set of 15 large scale unconstrained test problems with problem size taken as up to 1000. In order to see its efficiency over other population based algorithms, the results are compared with SOMA and particle swarm optimization algorithm (PSO). The comparative analysis shows the efficiancy of the proposed algorithm to solve large scale function optimization with less function evaluations.

C-SOMAQI: Self Organizing Migrating Algorithm with Quadratic Interpolation Crossover Operator for Constrained Global Optimization

SOMAQI is a variant of Self Organizing Migrating Algorithm (SOMA) in which SOMA is hybridized with Quadratic Interpolation crossover operator, presented by Singh et al. (Advances in intelligent and soft computing. Springer, India, pp. 225–234, 2014). The algorithm SOMAQI has been designed to solve unconstrained nonlinear optimization problems. Earlier it has been tested on several benchmark problems and the results obtained by this technique outperform the results taken by several other techniques in terms of population size and function evaluations. In this chapter SOMAQI has been extended for solving constrained nonlinear optimization problems (C-SOMAQI) by including a penalty parameter free approach to select the feasible solutions. This algorithm also works with small population size and converges very fast. A set of 10 constrained optimization problems has been used to test the performance of the proposed algorithm. These problems are varying in complexity. To validate the efficiency of the proposed algorithm results are compared with the results obtained by C-SOMGA and C-SOMA. On the basis of the comparison it has been concluded that C-SOMAQI is efficient to solve constrained nonlinear optimization problems.

Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications

Optimization techniques have developed into a modern-day solution for real-world problems in various industries. As a way to improve performance and handle issues of uncertainty, optimization research becomes a topic of special interest across disciplines. Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications presents the latest research trends and developments in the area of applied optimization methodologies and soft computing techniques for solving complex problems. Taking a multi-disciplinary approach, this critical publication is an essential reference source for engineers, managers, researchers, and post-graduate students.

Log-logistic SOMA with quadratic approximation crossover

Though population based algorithms performs well to solve many global optimization problems, many attempts has been made in literature to improve the efficiency of these algorithms. One possible way is to hybridized them with the features of other deterministic or population based techniques. This Paper presents a Log-LogisticSelf organizing migrating algorithm with quadratic approximation crossover (LLSOMAQI). This algorithm is an extension of algorithms SOMAQI, in which Self Organizing Migrating Algorithm (SOMA) has been hybridized with quadratic approximation (QA) crossover operator and SOMA-M, which is hybridization of SOMA and Log-Logistic (LL)mutation. LLSOMAQI has been tested on 15 benchmark unconstrained test problems and an analysis has been made between the three algorithms. LLSOMAQI, its originator SOMA and PSO to show the efficiency of this algorithm over other population based algorithms.

Hybridization of Self Organizing Migrating Algorithm with Quadratic Approximation and Non Uniform Mutation for Function Optimization

Self-organizing migrating algorithm (SOMA) is relatively a new population-based stochastic search technique for solving nonlinear global optimization problems. There has been done very less work on hybridization of SOMA with other methodologies in order to improve its performance. This paper presents hybridization of self-organizing migrating algorithm with quadratic approximation or interpolation (SOMAQI) and non-uniform mutation. This hybridization (M-SOMAQI) uses the quadratic interpolation (QI) and non-uniform mutation for creating a new solution vector in the search space. To validate the efficiency of this algorithm, it is tested on 15 benchmark test problems taken from the literature, and the obtained results are compared with SOMA and the SOMAQI. The numerical and graphical results conclude that the presented algorithm shows better performance in terms of population size, efficiency, reliability, and accuracy.

Optimization Models for Solid Waste Management in Indian Cities: A Study

India is a developing country. India’s population is over 1.27 billion people (2014) which are approximated as one-sixth of the world’s population. Such population growth may leave behind the world’s most heavily populated nation China by 2025 (US Census Bureau 2011). Solid waste management is an important environmental issue of all developed and developing countries. The growth of solid waste is basically due to population explosion, urbanization, and mismanagement of municipal corporation. Limitations of Indian Municipality Corporation are waste gathering inefficiency, lack of financial funds, poor planning, and lack of technical knowledge on changing complication of waste materials. In this paper, few important optimization models/techniques proposed by different researchers are studied that may be beneficial for ongoing project work in Municipal solid waste management (MSWM) in different metropolitan cities at various states in India. Much work has not been done in this direction. On the basis of extensive study of literature, it is suggested that the Indian municipal corporation must adopt 4R principles involving reduce, reuse, recycle, and recover to minimize solid waste.

Seismic Analysis of Multistoried Building with Optimized Damper Properties

In today’s scenario where space is an issue, the increase in population has led to a boom in the construction industry. With the lack of land for construction, the buildings are becoming higher and more complex, so with the increase in the number of stories, it is necessary to make them safe under adverse seismic conditions. Dampers are one way to make the structure earthquake resistant and the optimization of their properties is sometimes required. In this study, the damper properties, i.e., damping and stiffness have been optimized using self-organizing migrating genetic algorithm (SOMGA) and genetic algorithm (GA) technique on a model of 10-storey building which has equal mass, stiffness, etc. on all the floors. The optimized damper properties obtained from SOMGA result in the reduction of 52% of the storey displacement while that of GA is 60% as compared to the undamped model. Both techniques provide better optimized damper properties. It is observed that the optimized damper helps in significant reduction of the seismic response of the structure, thus justifying the need of optimized parameters of dampers.