Pradeep Jangir

Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems

This paper proposes a multi-objective version of the recently proposed Ant Lion Optimizer (ALO) called Multi-Objective Ant Lion Optimizer (MOALO). A repository is first employed to store non-dominated Pareto optimal solutions obtained so far. Solutions are then chosen from this repository using a roulette wheel mechanism based on the coverage of solutions as antlions to guide ants towards promising regions of multi-objective search spaces. To prove the effectiveness of the algorithm proposed, a set of standard unconstrained and constrained test functions is employed. Also, the algorithm is applied to a variety of multi-objective engineering design problems: cantilever beam design, brushless dc wheel motor design, disk brake design, 4-bar truss design, safety isolating transformer design, speed reduced design, and welded beam deign. The results are verified by comparing MOALO against NSGA-II and MOPSO. The results of the proposed algorithm on the test functions show that this algorithm benefits from high convergence and coverage. The results of the algorithm on the engineering design problems demonstrate its applicability is solving challenging real-world problems as well.

Optimal power flow with enhancement of voltage stability and reduction of power loss using ant-lion optimizer

Abstract In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic optimization algorithm ant-lion optimizer (ALO). ALO is inspired by the hunting process of ant-lions in the natural environment. ALO has a fast convergence rate due to the use of roulette wheel selection method. For the solution of the optimal power flow problem, standard 30 bus IEEE system is used. ALO is applied to solve the suggested problem. The problems considered in the OPF problem are fuel cost reduction, voltage profile improvement, voltage stability enhancement, minimization of active power losses and minimization of reactive power losses. The results obtained with ALO is compared with other methods like firefly algorithm (FA) and particle swarm optimization (PSO). Results show that ALO gives better optimization values as compared with FA and PSO which verifies the strength of the suggested algorithm.

Optimization of problems with multiple objectives using the multi-verse optimization algorithm

This work proposes the multi-objective version of the recently proposed Multi-Verse Optimizer (MVO) called Multi-Objective Multi-Verse Optimizer (MOMVO). The same concepts of MVO are used for converging towards the best solutions in a multi-objective search space. For maintaining and improving the coverage of Pareto optimal solutions obtained, however, an archive with an updating mechanism is employed. To test the performance of MOMVO, 80 case studies are employed including 49 unconstrained multi-objective test functions, 10 constrained multi-objective test functions, and 21 engineering design multi-objective problems. The results are compared quantitatively and qualitatively with other algorithms using a variety of performance indicators, which show the merits of this new MOMVO algorithm in solving a wide range of problems with different characteristics.

An emission constraint Economic Load Dispatch problem solution with Microgrid using JAYA algorithm

In this Work, the Distributed Energy Resources (DERs) are used in a specific small area which is known a microgrid. 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 meta-heuristic techniques. The CEED is the procedure to scheduling the generating units within their bounds together with minimizing the fuel cost and emission values. The JAYA Algorithm 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 JAYA Algorithm 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 JAYA Algorithm gives comparative better cost reduction 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.

Economic Load Dispatch problem with ramp rate limits and prohibited operating zones solve using Levy flight Moth-Flame optimizer

A novel bio-inspired optimization algorithm based on the navigation strategy of Moths in universe called the Moth-Flame optimization (MFO) Algorithm, is exercised for Economic Load Dispatch problems. The navigating mechanism of moths in nature called transverse orientation, a very effective mechanism for traveling long distances in the straight direction. In fact, artificial lights trick moths, so they follow a deadly spiral path. MFO algorithm is integrated with levy flights to achieve the competitive results in case of both discrete and continuous control parameters. Levy flights or Levy distribution is a sophisticated randomization technique has a relevant role in both exploration and exploitation. Constrained optimization is a way of optimizing an objective function in presence of constraints on some control parameters. So Levy flights or Levy distribution is a sophisticated randomization technique is integrated with MFO and exercised on highly complex constrained economic load dispatch (ELD) problem with three different standard case study of 6 and 15 generating unit system without valve point effect including ramp rate limits (RRL) and prohibited operating zones (POZ) and 13-unit system with valve point effect loading. Integration of randomization Levy flights technique provides potential that Levy flights based MFO algorithm to attain optimal solution and faster convergence over standard MFO optimization algorithms.

Energy management of Renewable Energy Sources in a microgrid using Cuckoo Search Algorithm

In this paper, the Renewable Energy Resources (RES) are used in a specific small area which is known as microgrid. Microgrid consists of microsources, battery storage and loads. In this paper the research is focused on the islanded mode microgrid. In the microgrid, the energy management system having a problem of Economic Load Dispatch (ELD) and it is optimized by meta-heuristic techniques. The newly introduced Cuckoo Search Algorithm (CSA) is implemented for the solution of ELD problem in the MATLAB environment. The minimization of total cost is obtained for four different scenarios like all sources included, all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In both scenarios, the result shows the comparison of CSA with the Reduced Gradient Method (RGM) for the ELD problem solution. The results are calculated for different power demand of 24 hours. The results obtained with CSA gives better cost reduction in less iterations as compared to RGM which shows the effectiveness of the suggested algorithm.

Voltage stability enhancement and voltage deviation minimization using BAT optimization algorithm

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic BAT Optimization Algorithm (BOA). BOA is inspired by the echolocation capability of micro-bats. BOA has a fast convergence rate. In order to solve the optimal power flow problem, standard IEEE-30 bus test system is used. BOA is implemented for the solution of proposed problem. The problems considered in the OPF problem are Fuel Cost Reduction, Voltage Deviation Minimization, and Voltage Stability Improvement. The results obtained by BOA is compared with other techniques such as Flower Pollination Algorithm (FPA) and Particle Swarm Optimizer (PSO). Results shows that BOA gives better optimization values as compared with FPA and PSO that confirms the effectiveness of the suggested algorithm.

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.

Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel metaheuristic optimization algorithm Moth-Flame Optimizer (MFO). The MFO is inspired by the navigation strategy of moths in universe. MFO has a fast convergence rate due to the use of roulette wheel selection method. For the OPF solution, standard IEEE-30 bus test system is used. MFO is applied to solve the proposed problem. The problems considered in the OPF are fuel cost reduction, voltage profile improvement, voltage stability enhancement, active power loss minimization and reactive power loss minimization. The results obtained by MFO are compared with other techniques such as Flower Pollination Algorithm (FPA) and particle swarm optimizer (PSO). Results show that MFO gives better optimization values as compared with FPA and PSO which verifies the effectiveness of the suggested algorithm.

Optimal active and Reactive Power dispatch problem solution using Moth-Flame Optimizer algorithm

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic optimisation algorithm Moth-Flame Optimizer (MFO). MFO is inspired by the navigation strategy of Moths in universe. MFO has a fast convergence rate due to use of roulette wheel selection method. In order to resolve the optimal power flow problem, standard IEEE-30 bus system is used. MFO is implemented for the solution of proposed problem. The problems considered in the OPF problem are Fuel Cost Reduction, Active Power Loss Minimization, and Reactive Power Loss Minimization. The results obtained by MFO is compared with other techniques such as Flower Pollination Algorithm (FPA) and Particle Swarm Optimizer (PSO). Results shows that MFO gives better optimisation values as compared with FPA and PSO that confirms the effectiveness of the suggested algorithm.