Browsing by Author "Ojha A.K."

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An approach to solve multi-objective linear fractional programming problem
(2016) Nayak S.; Ojha A.K.
In this paper, an approach of hybrid technique is presented to derive Pareto optimal solutions of a multi-objective linear fractional programming problem (MOLFPP). Taylor series approximation along with the use of a hybrid technique comprising both weighting and ?-constraint method is applied to solve the MOLFPP. It maintains both priority and achievement of possible aspired values of the objectives by the decision maker (DM) while producing Pareto optimal solutions. An illustrative numerical example is discussed to demonstrate the proposed method and to justify the effectiveness, the results so obtained are compared with existing fuzzy max�min operator method. � Springer Science+Business Media Singapore 2016.
A comparative study on optimization techniques for solving multi-objective geometric programming problems
(2015) Ota R.R.; Ojha A.K.
Various optimization techniques are there for mathematical model- ing and design of many non-linear problems related to the various field of science and engineering. The real world engineering problems are oc- curring in the form of multi-criteria having certain constraints. Till now, no such single optimization method exist which could optimize all objec- tive functions simultaneously. In this paper, weighted sum method has been discussed to solve multi-objective geometric programming prob- lem(MOGPP) and the result so obtained by weighted sum method has been compared with fuzzy programming method. Illustrative examples are presented to demonstrate the correctness of proposed model. � 2014 Rashmi Ranjan Ota and A. K. Ojha.
Cornerity in binary image using neural network
(2009) Ojha A.K.; Mallick C.; Mallick D.
Image formation is an important features in Neural Network. But the detection of corner position for the formation of binary gray image has an important aspect in the present day.In this present paper we have made an attempt to develope network model to detect corner points in the polygonal surfaces in the formation of binary grary images.In the network model a pair of nodes corresponds to a pixel in the image. The output of the pair of nodes together represents the corner vector of the corresponding pixel by using neighborhood information.After the network dynamics settles to stable state the dominant points are obtained by finding out the local maxima in the cornerities.The theoretical investigations are made to study the stability and convergence of the network for the detection of corner position in image formation.The dynamics of the network is then extended to accept the cornering information to form binary images by embedding the edge strength information. A grace performance by the network is observed for the binary images. � SAS International Publications.
Geometric programming technique to optimize power distribution system
(2019) Ota R.R.; Pati J.C.; Ojha A.K.
Geometric programming is an important tool for solving certain optimization problems. In this paper, multi objective geometric programming with ?-constraint method is used to find the maximum radius of a circular power supply substation to supply power in a particular region. The main aim of the proposed method is to formulate a mathematical model for the efficient distribution of the power supply to maximum area from a circular substation with least investment and minimum waste. The proposed multi-objective optimization model has been solved to generate Pareto optimal solutions using weighted sum method. The results so obtained have been compared with that of ?-constraint method by considering suitable numerical examples. � 2019, Operational Research Society of India.
Goal attainment method and Taylor series approximation to solve multi-objective linear fractional programming problem
(2015) Nayak S.; Ojha A.K.
This paper illustrates the use of goal attainment method with Taylor series approximation to produce a set of Pareto optimal solutions of a multi-objective linear fractional programming problem (MOLFPP). The relative gap in between the aspired goal and the feasible objective space is minimised along different search directions by introducing several non-negative and normalised weight vectors. To justify the effectiveness of our proposed method, a practical problem with numerical example is solved and the results are compared with that of existing fuzzy max-min operator method. � 2015 Inderscience Enterprises Ltd.
A hybrid Cat Swarm Optimization with invasive weed optimization
(2015) Ojha A.K.; Naidu Y.R.
Cat Swarm Optimization (CSO) is a recent meta-heuristic optimization algorithm to obtain a global optimal solution. Sometimes the standard CSO converges to an optimal solution slowly and may not obtain the accurate solution. To avoid these drawbacks, Invasive weed optimization (IWO) is added to the CSO in tracing mode. � 2015 IEEE.
A hybrid method for solving multi-objective geometric programming problem
(2015) Ojha A.K.; Ota R.R.
A multi-objective geometric programming problem contains more than one objective that needs to be achieved simultaneously. Such problems arise in many applications where two or more, sometimes conflicting objective functions have to be minimised concurrently. In this paper a new adaptive strategy called hybrid method proposed to find Pareto optimal solutions of the multi-objective geometric programming problem. Using geometric programming technique, a global best optimal solution is obtained from a set of Pareto optimal solution having a great impact on convergence of solution. The discussed hybrid method having a goal to enhance the optimisers over all performance by combining different optimisation techniques. In the proposed method we have combined ?-constraint and weighted mean method and finally the result so obtained compared with the result obtained by fuzzy programming method. The solution procedure of the proposed hybrid method is illustrated by the numerical examples. Copyright � 2015 Inderscience Enterprises Ltd.
A hybrid version of invasive weed optimization with quadratic approximation
(2015) Naidu Y.R.; Ojha A.K.
Invasive weed optimization (IWO) is a recent meta-heuristic optimization technique, based on the life cycle of plants. It has been applied in many engineering applications as well as in real world problems. In this paper, a hybrid version of IWO with the quadratic approximation (QA) operator, referred as QAIWO, has been investigated to improve the convergence rate of IWO while obtaining optimal solution. Additionally, we alleviate the limitation of QA (which is nothing but difficulty in escaping from a local optimum) by performing QA a predetermined number of times and then considering the average of all such solutions due to each iteration rather than a single solution. This technique makes our algorithm more efficient compared to the existing algorithms in the area. Twenty two benchmark problems and five real-life problems are adopted from literature to validate our proposed hybrid method QAIWO. The results of QAIWO are compared with the results obtained by the standard IWO and the well-known nature-inspired genetic algorithm (GA). These comparisons exhibit that QAIWO is more convenient to solve complex problems than using IWO and/or GA. � 2015, Springer-Verlag Berlin Heidelberg.
Hybridizing particle swarm optimization with invasive weed optimization for solving nonlinear constrained optimization problems
(2015) Ojha A.K.; Naidu Y.R.
Most of engineering applications are occurring in the form of nonlinear constrained optimization problems. They have to be solved in point of accuracy and faster convergence. In this paper, the combination of particle swarm optimization (PSO) and invasive weed optimization (IWO) is discussed and the stochastic ranking method is incorporated to handle the constraints, named as a PSO-IWO-SR. Due to page limitation, four well-known nonlinear constrained optimization engineering design problems are adopted to validate the performance of the PSO-IWO-SR. The results obtained by the proposed method PSO-IWO-SR are better than the stateof- the-art evolutionary algorithms with respect to accuracy and computational time. � Springer India 2015.
Multi-objective geometric programming problem with Karush-Kuhn-Tucker condition using Iu-constraint method
(2014) Ojha A.K.; Ota R.R.
Optimization is an important tool widely used in formulation of the mathematical model and design of various decision making problems related to the science and engineering. Generally, the real world problems are occurring in the form of multi-criteria and multi-choice with certain constraints. There is no such single optimal solution exist which could optimize all the objective functions simultaneously. In this paper, Iu-constraint method along with Karush-Kuhn-Tucker (KKT) condition has been used to solve multi-objective Geometric programming problems(MOGPP) for searching a compromise solution. To find the suitable compromise solution for multi-objective Geometric programming problems, a brief solution procedure using �?-constraint method has been presented. The basic concept and classical principle of multi-objective optimization problems with KKT condition has been discussed. The result obtained by Iu-constraint method with help of KKT condition has been compared with the result so obtained by Fuzzy programming method. Illustrative examples are presented to demonstrate the correctness of proposed model. � 2014 EDP Sciences, ROADEF, SMAI.
Multi-objective geometric programming problem with {small element of}-constraint method
(2014) Ojha A.K.; Biswal K.K.
In multi-objective geometric programming problem there are more than one objective functions. There is no single optimal solution which simultaneously optimizes all the objective functions. Under these conditions the decision makers always search for the most "preferred" solution, in contrast to the optimal solution. A few mathematical programming methods namely fuzzy programming, goal programming and weighting methods have been applied in the recent past to find the compromise solution. In this paper {small element of}-constraint method has been applied to find the non-inferior solution. A brief solution procedure of {small element of}-constraint method has been presented to find the non-inferior solution of the multi-objective programming problems. Further, the multi-objective programming problems is solved by the fuzzy programming technique to find the optimal compromise solution. Finally, two numerical examples are solved by both the methods and compared with their obtained solutions. � 2013 Elsevier Inc.
Multi-objective Jaya Algorithm for Solving Constrained Multi-objective Optimization Problems
(2020) Ramu Naidu Y.; Ojha A.K.; Susheela Devi V.
Solving multi-objective optimization problems (MOPs) is a challenging task since they conflict with each other. In addition, incorporation of constraints to the MOPs, called CMOPs for a short, increases their complexity. Traditional multi-objective evolutionary algorithms (MOEAs) treat multiple objectives as a whole while solving them. By doing so, fitness assignment to each individual is difficult. In order to overcome this difficulty, in this paper, multiple populations are considered for multiple objectives to optimize simultaneously and a hybrid method of Jaya algorithm (JA) and quasi reflected opposition based learning (QROBL), to maintain diversity among populations, is used as an optimizer to solve CMOPs. An archive is also used to store all non-dominated solutions and to guide the search towards the Pareto front. A local search (LS) method is performed on the archive members to improve their quality and converge to the Pareto front. The whole above process is named as CMOJA. The obtained results are compared with the state-of-the-art algorithms and demonstrated that the proposed hybrid method has shown its superiority to its competitors. � 2020, Springer Nature Switzerland AG.
Multi-objective linear fractional programming problem with fuzzy parameters
(2019) Nayak S.; Ojha A.K.
In this paper, a method is developed to derive the acceptable ranges of objective values for a multi-objective linear fractional programming problem(MOLFPP) with fuzzy parameters both in objectives and constraints. ? - and ? -cuts are respectively used in the objectives and constraints to specify the degrees of satisfaction and transform the fuzzy parameters into closed intervals. Using variable transformation and Taylor series expansion, the interval-valued fractional objectives are approximated by intervals of linear functions. The objective functions are assigned proper weights using analytic hierarchy process. Weighting sum method is used to transform the interval-valued multiple objectives into single objective. MOLFPP in interval-valued form is equivalently formulated as two linear problems which derive the acceptable ranges of objective values. Two numerical examples are illustrated to demonstrate the proposed method. � Springer Nature Singapore Pte Ltd. 2019.
Preface
(2019) Bansal J.C.; Das K.N.; Nagar A.; Deep K.; Ojha A.K.
[No abstract available]
Preface
(2019) Bansal J.C.; Das K.N.; Nagar A.; Deep K.; Ojha A.K.
[No abstract available]
A solution approach to multi-level nonlinear fractional programming problem
(2018) Nayak S.; Ojha A.K.
This paper studies multi-level nonlinear fractional programming problem (ML-NLFPP) of maximization type and proposes a solution approach which is based on the concept of fuzzy and simultaneous minimization, maximization of the objectives from their ideal, anti-ideal values, respectively. Nonlinear polynomial functions are considered as the numerators and denominators of the fractional objectives at each level. In the objective space, distance function or Euclidean metric is implemented to measure the distances between numerators, denominators and their ideal, anti-ideal values which need to be minimized and maximized. Goals for the controlled decision variables of upper levels are ascertained from the individual best optimal solutions of the corresponding levels, and tolerances are defined by decision makers to avoid the situation of decision deadlock. Fuzzy goal programming with reduction of only under-deviation from the highest membership value derives the best compromise solution of the concerned multi-level problem. An illustrative numerical example is discussed to demonstrate the solution approach and its effectiveness. � 2018, Springer Nature Singapore Pte Ltd.
Solution approach to multi-objective linear fractional programming problem using parametric functions
(2019) Nayak S.; Ojha A.K.
In this paper, an iterative technique based on the use of parametric functions is proposed to obtain the best preferred optimal solution of a multi-objective linear fractional programming problem. The decision maker ascertains own desired tolerance values for the objectives as termination constants and imposes them on each iteratively computed objective functions in terms of termination conditions. Each fractional objective is transformed into non-fractional parametric function using certain initial values of parameters. The parametric values are iteratively computed and ?-constraint method is used to obtain the pareto (weakly) optimal solutions in each step. The computations get terminated when all the termination conditions are satisfied at a pareto optimal solution of an iterative step. A numerical example is discussed at the end to illustrate the proposed method and fuzzy max�min operator method is applied to validate the obtained results. � 2019, Operational Research Society of India.
Solving Multiobjective Optimization Problems Using Hybrid Cooperative Invasive Weed Optimization with Multiple Populations
(2018) Ramu Naidu Y.R.; Ojha A.K.
In this paper, hybridization of invasive weed optimization (IWO) and space transformation search (STS) are presented to solve, by applying multiple populations for multiple objectives individually, multiobjective optimization. This whole process is addressed as hybrid cooperative multiobjective optimization IWO (HCMOIWO). We carried out an application to solve system of nonlinear equations. In HCMOIWO, M single objectives are optimized simultaneously using the hybrid IWO with STS and all the nondominated solutions that are extracted from the group of parent weeds and offspring are stored in an archive, A. This archive is used not only to store nondominated solutions, but also to exchange information among subpopulations to explore the new search areas along the Pareto front. To exploit the nondominated solutions, a local search technique is adopted in HCMOIWO. The performance of HCMOIWO is evaluated with different sets of benchmark problems having different characteristics. Empirical results reveal the supremacy of HCMOIWO over state-of-the-art algorithms reported in recent literature. � 2013 IEEE.
Solving nonlinear constrained optimization problems by Invasive Weed Optimization using penalty function
(2014) Naidu Y.R.; Ojha A.K.
This paper discusses the application of nature inspired optimization technique for nonlinear constrained optimization problems (NCPP). Here the technique of Invasive Weed Optimization Algorithm (IWO) is chosen with the Simulated Annealing penalty method. In Simulated Annealing penalty method, the penalty function increases with generation number as the infeasible solution is forced towards the feasible region. This paper has reported the capability of IWO with Simulated Annealing penalty method for six bench mark problems. The results demonstrate the superiority of the proposed method over the previously published results. � 2014 IEEE.
Solving Nonlinear Constrained Optimization Problems Using Invasive Weed Optimization
(2015) Naidu Y.R.; Ojha A.K.
Many real-world problems are constrained optimization problems. In solving nonlinear constrained optimization problems, penalty function method has been the popular approach. The performance of invasive weed optimization (IWO) with multistage penalty function is discussed in this paper. The proposed IWO is performed for six well-known problems, and results are reported. The obtained results demonstrate that IWO outperformed than other evolutionary algorithms. � Springer India 2015.