A Metaheuristic Approach for the Problem of Motor Fuel Distribution

AbstractMotor fuel distribution problem is considered. Accepting some assumptions it can be reduced to a well-known vehicle routing problem with capacity constraints. Ant colony optimization approach is suggested for solving CVRP. Modified ant algorithms are performed. Computational results for some benchmarks are given in compare with classical ant algorithm

Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

The main objective of thermoelectric power plants is to meet the power demand with the lowest fuel cost and emission levels of pollutant and greenhouse gas emissions, considering the operational restrictions of the power plant. Optimization techniques have been widely used to solve engineering problems as in this case with the objective of minimizing the cost and the pollution damages. Heuristic and metaheuristic algorithms have been extensively studied and used to successfully solve this multi-objective problem. This chapter, several optimization techniques (simulated annealing, ant lion, dragonfly, NSGA II, and differential evolution) are analyzed and their application to economic-emission load dispatch (EELD) is also discussed. In addition, a comparison of all approaches and its results are offered through a case study

Improved decision support for engine-in-the-loop experimental design optimization

Experimental optimization with hardware in the loop is a common procedure in engineering and has been the subject of intense development, particularly when it is applied to relatively complex combinatorial systems that are not completely understood, or where accurate modelling is not possible owing to the dimensions of the search space. A common source of difficulty arises because of the level of noise associated with experimental measurements, a combination of limited instrument precision, and extraneous factors. When a series of experiments is conducted to search for a combination of input parameters that results in a minimum or maximum response, under the imposition of noise, the underlying shape of the function being optimized can become very difficult to discern or even lost. A common methodology to support experimental search for optimal or suboptimal values is to use one of the many gradient descent methods. However, even sophisticated and proven methodologies, such as simulated annealing, can be significantly challenged in the presence of noise, since approximating the gradient at any point becomes highly unreliable. Often, experiments are accepted as a result of random noise which should be rejected, and vice versa. This is also true for other sampling techniques, including tabu and evolutionary algorithms. After the general introduction, this paper is divided into two main sections (sections 2 and 3), which are followed by the conclusion. Section 2 introduces a decision support methodology based upon response surfaces, which supplements experimental management based on a variable neighbourhood search and is shown to be highly effective in directing experiments in the presence of a significant signal-to-noise ratio and complex combinatorial functions. The methodology is developed on a three-dimensional surface with multiple local minima, a large basin of attraction, and a high signal-to-noise ratio. In section 2, the methodology is applied to an automotive combinatorial search in the laboratory, on a real-time engine-in-the-loop application. In this application, it is desired to find the maximum power output of an experimental single-cylinder spark ignition engine operating under a quasi-constant-volume operating regime. Under this regime, the piston is slowed at top dead centre to achieve combustion in close to constant volume conditions. As part of the further development of the engine to incorporate a linear generator to investigate free-piston operation, it is necessary to perform a series of experiments with combinatorial parameters. The objective is to identify the maximum power point in the least number of experiments in order to minimize costs. This test programme provides peak power data in order to achieve optimal electrical machine design. The decision support methodology is combined with standard optimization and search methods – namely gradient descent and simulated annealing – in order to study the reductions possible in experimental iterations. It is shown that the decision support methodology significantly reduces the number of experiments necessary to find the maximum power solution and thus offers a potentially significant cost saving to hardware-in-the-loop experi- mentation

Metaheuristics for online drive train efficiency optimization in electric vehicles

Utilization of electric vehicles provides a solution to several challenges in today’s individual mobility. However, ensuring maximum efficient operation of electric vehicles is required in order to overcome their greatest weakness: the limited range. Even though the overall efficiency is already high, incorporating DC/DC converter into the electric drivetrain improves the efficiency level further. This inclusion enables the dynamic optimization of the intermediate voltage level subject to the current driving demand (operating point) of the drivetrain. Moreover, the overall drivetrain efficiency depends on the setup of other drivetrain components’ electric parameters. Solving this complex problem for different drivetrain parameter setups subject to the current driving demand needs considerable computing time for conventional solvers and cannot be delivered in real-time. Therefore, basic metaheuristics are identified and applied in order to assure the optimization process during driving. In order to compare the performance of metaheuristics for this task, we adjust and compare the performance of different basic metaheuristics (i.e. Monte-Carlo, Evolutionary Algorithms, Simulated Annealing and Particle Swarm Optimization). The results are statistically analyzed and based on a developed simulation model of an electric drivetrain. By applying the bestperforming metaheuristic, the efficiency of the drivetrain could be improved by up to 30% compared to an electric vehicle without the DC/DC- converter. The difference between computing times vary between 30 minutes (for the Exhaustive Search Algorithm) to about 0.2 seconds (Particle Swarm) per operating point. It is shown, that the Particle Swarm Optimization as well as the Evolutionary Algorithm procedures are the best-performing methods on this optimization problem. All in all, the results support the idea that online efficiency optimization in electric vehicles is possible with regard to computing time and success probability

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Multi-Objective Optimization of Solar Powered Irrigation System by Using Genetic Algorithm

Irrigation system is synonym with agriculture. Conventional way of supplying source of energy to work the water pumping system is through fuel combustion such as diesel. Nowadays fuel combustion is not an attractive and feasible approach in a long run due to hiking fuel price and it is also not environmentally friendly which it may lead to pollution. The development of renewable energy such as solar energy as an external heat source rather be more attractive. However, this complex system needs to be optimized by using suitable metaheuristic technique in order to make the design to be economically and practically efficient. Thus, Genetic Algorithm is applied to solve multiple objective solar-irrigation system optimization. It is identified that the best setting should be input to get an optimal solution. Initial range of [1; 2] and crossover fraction of 1.0 have majorly contributed to the optimal search parameters. After some tuning to get the best setting, the simulation shows that the fitness function of 3 objectives resulted with 17.4303 kW power output, 15.2355% efficiency and $143,533.10 fiscal savings. This set of optimal solution is not as closed as other technique to the desired design objectives. Genetic Algorithm is a common technique and easy to work with but it has yet to be the best metaheuristic technique for this engineering problem due to some drawback

Integrating forecasting in metaheuristic methods to solve dynamic routing problems: evidence from the logistic processes of tuna vessels

The multiple Traveling Salesman Problem (mTSP) is a widespread phenomenon in real-life scenarios, and in fact it has been addressed from multiple perspectives in recent decades. However, mTSP in dynamic circumstances entails a greater complexity that recent approaches are still trying to grasp. Beyond time windows, capacity and other parameters that characterize the dynamics of each scenario, moving targets is one of the underdeveloped issues in the field of mTSP. The approach of this paper harnesses a simple prediction method to prove that integrating forecasting within a metaheuristic evolutionary-based method, such as genetic algorithms, can yield better results in a dynamic scenario than their simple non-predictive version. Real data is used from the retrieval of Fish Aggregating Devices (FADs) by tuna vessels in the Indian Ocean. Based on historical data registered by the GPS system of the buoys attached to the devices, their trajectory is firstly forecast to feed subsequently the functioning of a genetic algorithm that searches for the optimal route of tuna vessels in terms of total distance traveled. Thus, although valid for static cases and for the Vehicle Routing Problem (VRP), the main contribution of this method over existing literature lies in its application as a global search method to solve the multiple TSP with moving targets in many dynamic real-life optimization problems.Ministerio de Economía y Competitividad | Ref. ECO2016-76625-RXunta de Galicia | Ref. GRC2014/02