R.H. Bhesdadiya

Moth-Flame optimization Algorithm for solving real challenging constrained engineering optimization problems

In this paper, a novel nature-inspired optimization algorithm based on the navigation strategy of Moths in universe called the Moth-Flame optimization (MFO) Algorithm, is applied for constrained optimization and engineering design problems. A comparative analysis of MFO algorithm expresses the optimum functional value in term of accuracy and standard deviation over rest of well-known constraint optimization algorithms. Five constrained benchmark function of engineering problems have been calculated and gained solutions were compared with other recognized algorithms. The gained solution expresses that MFO algorithm provides better results in various design problems compared to other optimization algorithms.

An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem

Abstract The main ambition of utility is to provide continuous reliable supply to customers, satisfying power balance, transmission loss while generators are allowed to be operated within rated limits. Meanwhile, achieving this from fossil fuel fired power plant emission value and fuel cost should be as less as possible. An allowable deviation in fuel cost and feasible tolerance in fuel cost has been additively called as multi objective combined economic emission dispatch (MOCEED) problem. MOCEED problem is applied to newly proposed non dominated sorting genetic algorithm-III (NSGA-III). NSGA-III method is really powerful to handle problems with non-linear characteristics as well as having many objectives. The proposed NSGA-III is firstly applied to unconstraint/constraints multi-objective test functions then applied to solve MOCEED problem with 6-generation unit, IEEE 118 bus 14 generating unit system with a smooth quadratic fuel/emission objective functions and 10-unit with non-smooth/valve point loading effect test system. Statistical results of MOCEED problem obtained by NSGA-III is compared with other well-known techniques proposed in recent literature, validates the effectiveness of proposed approach.

A Novel Hybrid Approach Particle Swarm Optimizer with Moth-Flame Optimizer Algorithm

Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new method hybrid PSO (Particle Swarm Optimization)—MFO (Moth-Flame Optimizer) is exercised on some unconstraint benchmark test functions and overcurrent relay coordination optimization problems in contrast to test results on constrained/complex design problem. Hybrid PSO-MFO is combination of PSO used for exploitation phase and MFO for exploration phase in uncertain environment. Position and Velocity of particle is updated according to Moth and flame position in each iteration. Analysis of competitive results obtained from PSO-MFO validates its effectiveness compare to standard PSO and MFO algorithm.

An emission constraint environment dispatch problem solution with microgrid using Whale Optimization Algorithm

In this work, microgrid is modern small scale power system of the centralized electricity for a small community such as villages and commercial area. Microgrid consists of microsources like distribution generator, solar and wind units, etc., and different loads. In the microgrid, the energy management system (EMS) having a problem of Combined Economic Emission Dispatch (CEED) and it is optimized by metaheuristic techniques. The CEED is the procedure to scheduling the generating units within their bounds together with minimizing the fuel cost and emission values. The Whale Optimization Algorithm (WOA) is applied for the solution of CEED problem in the MATLAB environment. The minimization of total cost and total emission are obtained for all sources included. The result shows the comparison of WOA with the Gradient Method (GM), Ant Colony Optimization (ACO) and Particle Swarm Optimizer (PSO) technique for the two different cases which are Economic Load Dispatch (ELD) without emission and with emission. The results are calculated for different power demand of 24 hours. The results obtained with WOA gives better cost reduction in less iterations as compared to GM, ACO and PSO which shows the effectiveness of the given algorithm. The key objective of this work is to solve the CEED problem to obtained optimal system cost.

Price penalty factors based approach for combined economic emission dispatch problem solution using Dragonfly Algorithm

In The main ambition of utility is to provide continuous reliable supply to customers, satisfying power balance, transmission loss while generators are allowed to be operated within rated limits. Meanwhile, achieve this purpose emission value and fuel cost should be as less as possible. An allowable deviation in fuel cost and feasible tolerance in fuel cost has been called emission constrained economic dispatch (ECED) problem. A new nature-inspired Dragonfly Algorithm (DA) is based on concept of swarming behaviour is applied to solve ECED problem. ECED is a multi-criteria problem can transformed to single criteria using price penalty factor method. In this paper formulates quadratic function together with emission value and fuel cost are considered for multi-criteria problem. The effect of six penalty factors like “Min-Max”, “Max-Max” “Min-Min” “Max-Min” price penalty factors and emission value of various pollutants gases exhalation are included. The emission constrained economic dispatch (ECED) problem is analysed for an IEEE-30 Bus system with six operational generators. Results prove capability of DA in solving ECED problem with different penalty factors.

Solution of Optimal Power Flow with voltage stability enhancement using Grey Wolf Optimization

For the operation and planning of the power system optimal power flow is main tool. Optimal power flow is highly constrained and non-linear problem of the power system. So for the solution of OPF problem different techniques are reported in literature review. In this proposed paper we introduced a new nature inspired meta-heuristics Grey Wolf Optimization algorithm for the solution of OPF problem. Grey Wolf Optimizer was inspired by the hunting behavior of the grey wolves. In this proposed paper we are consider IEEE-30 bus test system for determining the effectiveness of proposed algorithm and we are considering total five different cases or five objectives. Then we compared proposed algorithm with some well-known algorithm which was reported in literature. From this analysis we realized that proposed GWO algorithm gave the best solution for all over the five cases with compared to some well-known evolutionary algorithm.

Optimal Power Flow Problem Solution Using Multi-objective Grey Wolf Optimizer Algorithm

The optimal power flow is highly constrained, nonlinear, and non-convex optimization problem of power system. This paper proposed multi-objective grey wolf optimizer (MOGWO) algorithm to evaluate optimal power flow (OPF) problems. Three objective functions such as emission, fuel cost, and active power loss are preferred as single-objective OPF problems. Proposed MOGWO algorithm was used to find pareto-optimal solution for two different multi-objective cases like Minimization of Fuel cost with Emission value and Minimization of Fuel cost with Active Power loss. The proposed MOGWO algorithm was tested on standard IEEE-30 bus test system with above two multi-objective functions to determine the efficiency of proposed algorithm. The outcomes of MOGWO algorithm were related with well-known NSGA-II (Non-dominated Sorting Genetic Algorithm) which was reported in the literature. MOGWO algorithm is given fast convergence and best pareto-optimal front setting with compared to NSGA-II.

A Novel Hybrid PSO-DA Algorithm for Global Numerical Optimization

Recent trend of research is to hybridize two and more algorithms to obtain superior solution in the field of optimization problems. In this context, a new technique hybrid Particle Swarm Optimization (PSO)—Dragonfly Algorithm (DA) is exercised on some unconstraint benchmark test functions and overcurrent relay co-ordination optimization problems in contrast to test results on constrained/complex design problem. Hybrid PSO-DA is combination of PSO used for exploitation phase and DA for exploration phase in uncertain environment. Position and Velocity of particle is updated according to position of dragonflies in each iteration. Analysis of competitive results obtained from PSO-DA validates its effectiveness compare to standard PSO and DA algorithm separately.

Training Multilayer Perceptrons in Neural Network Using Interior Search Algorithm

Multilayer perceptron (MLP) is the most popular neural network method and it has been widely used for many practical applications. In this paper, recently developed interior search algorithm (ISA) is proposed for training MLP. Five of most important standard classification datasets (balloon, XOR, Iris, heart, and breast cancer) are employed to evaluate the proposed algorithm performance. The obtained results from ISA-based are compared with five well-known algorithms including ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO), population-based incremental learning (PBIL), and evolution strategy (ES). The statistical results reflect that the performance of the proposed algorithm can train MLPs with a very high degree of accuracy and it is capable of outperforming the well-known algorithms. The results also show that the high convergence rate of the ISA and it is potential to avoid local minima.

Moth-Flame Optimizer Method for Solving Constrained Engineering Optimization Problems

A recently proposed swarm inspired optimization algorithm based on the navigation approach of Moths in space entitle as Moth-Flame Optimization (MFO) algorithm, is used for solve equality and inequality constrained optimization, real challenging layout problems. The navigating strategy of moths in universe entitles transverse orientation, a well active mechanism for travel so far distance in the straight direction. In fact, artificial lights trick moths, so they follow a deadly spiral path. MFO algorithm gives the competitive results with both continuous and discrete control variables. Real Challenging Constrained Optimization is a way of optimising an objective function in presence of constraints on some control variables. MFO have an ability to solve both constraints that may be either hard constrained or soft constrained. A statical representation of MFO algorithm expresses the best objective function value with reference to accuracy and standard deviation over recently proposed and most popular optimization algorithms. Fourteen constrained benchmark function of real engineering problems have been calculated and gained solutions were compared with the solution obtained by various recognized algorithms. The results obtained through MFO algorithm represent better solutions in the field of engineering design problems among many recently developed algorithms.