Genetic Algorithm Linear Programming Research Articles

Optimal microgrid’s protection coordination considering N-1 contingency and optimum relay characteristics

There is a knowledge gap about the development of optimal coordination of microgrids, which considers all N-1 contingencies by using the smart selection of standard relay characteristics. This paper tries to fill such a knowledge gap by contributing to introduce a novel method to optimize the coordination of microgrids’ directional overcurrent relays (DOCRs), which smartly selects the time-current characteristic of relays and considers all system topologies. Achieving an optimal protection scheme of microgrids without any selectivity constraint under various topologies is the main purpose of this research. The proposed objective function of total operating time of DOCRs under various system topologies is linearized as the TDSs. The hybrid heuristic-linear programming algorithms (HHLPAs) are used to solve the mixed-integer non-linear programming (MINLP) problem. The decrease in the number of heuristic algorithm’s decision variables improves the performance of the proposed HHLPAs. The soft computing-based comparison of the hybrid genetic algorithm-linear programming (GA-LP) and the hybrid particle swarm optimization-linear programming (PSO-LP) is another contribution of this paper. About 80% decrease in the DOCRs’ operating time has been achieved by applying the proposed smart selection of standard relay characteristics (normally inverse, very inverse, and extremely inverse) in comparison to use of just normally inverse curve based on existing methods. The satisfaction of coordination constraints of optimum relay settings is validated based on the DIgSILENT protection simulations.

Genetic Programming-Based Code Generation for Arduino

This article describes a methodology for writing the program for the Arduino board using an automatic generator of assembly language routines that works based on a cooperative coevolutionary multi-objective linear genetic programming algorithm. The methodology is described in an illustrative example that consists of the development of the program for a digital thermometer organized on a circuit formed by the Arduino Mega board, a text LCD module, and a temperature sensor. The automatic generation of a routine starts with an input-output table that can be created in a spreadsheet. The following routines have been automatically generated: initialization routine for the text LCD screen, routine for determining the temperature value, routine for converting natural binary code into unpacked two-digit BCD code, routine for displaying a symbol on the LCD screen. The application of this methodology requires basic knowledge of the assembly programming language for writing the main program and some initial configuration routines. With the application of this methodology in the illustrative example, 27% of the program lines were written manually, while the remaining 73% were generated automatically. The program, produced with the application of this methodology, preserves the advantage of assembly language programs of generating machine code much smaller than that generated by using the Arduino programming language.

Reducing the scenarios of network topology changes for adaptive coordination of overcurrent relays using hybrid GA–LP

In this study, using the concept of setting groups (SGs), an adaptive protection scheme is proposed to increase the reliability of the system. Connection and disconnection of switches and distributed generators result in various scenarios for network topology changes. A hybrid genetic algorithm (GA) and linear programming (LP) method is utilised to solve the problem, where the GA, in a near-optimal manner, classifies the scenarios of the network topology changes into a limited number of SGs and the LP algorithm optimally coordinates the overcurrent relays within the SGs. Simulations are performed on a radial distribution network and a meshed distribution network. Although by increasing the number of SGs the average operating time of the relays is decreased, the number of changes in the relay settings is increased. Therefore, the multi-objective optimisation algorithm is used to determine, the desired number of SGs. The results show the efficiency of the proposed adaptive protection scheme.

Studying bloat control and maintenance of effective code in linear genetic programming for symbolic regression

Linear Genetic Programming (LGP) is an Evolutionary Computation algorithm, inspired in the Genetic Programming (GP) algorithm. Instead of using the standard tree representation of GP, LGP evolves a linear program, which causes a graph-based data flow with code reuse. LGP has been shown to outperform GP in several problems, including Symbolic Regression (SReg), and to produce simpler solutions. In this paper, we propose several LGP variants and compare them with a traditional LGP algorithm on a set of benchmark SReg functions from the literature. The main objectives of the variants were to both control bloat and privilege useful code in the population. Here we evaluate their effects during the evolution process and in the quality of the final solutions. Analysis of the results showed that bloat control and effective code maintenance worked, but they did not guarantee improvement in solution quality.

Identification of the Best Booster Station Network for a Water Distribution System

AbstractA fuzzy decision-making framework (DMF) is combined with a hybrid genetic algorithm–linear programming (GA-LP) optimization approach to determine the best booster station network for a water distribution system. The proposed hybrid GA-LP model simultaneously optimizes two conflicting objectives; namely, minimization of total chlorine injection dosage and the number of booster stations. At the same time, residual chlorine concentrations are kept within desired limits. Adjustment of the relative importance of two conflicting objectives results in different optimal solutions. Selection of the best alternative among these optimal solutions is performed through a fuzzy multiobjective DMF. The proposed DMF allows incorporation of the decision makers’ preferences into the booster station network design. In this study, three fuzzy objectives are selected based on economic, operational, and health-related concerns. The hybrid GA-LP model is applied to a case study, and results show that the proposed method...

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video console by using a publicly available common framework for the devices called XNA (for XNA's Not Acronymed). By constructing the LGP within this framework, we effectively produce an LGP game for PC and XBox 360 that displays results as they evolve. We use the GPU of each device to parallelize fitness evaluation and the mutation operator of the LGP algorithm, thus providing a general LGP implementation suitable for parallel computation on heterogeneous devices. While parallel GP implementations on PCs are now common, both the implementation of GP on a video console using GPU and the construction of a GP around a framework for heterogeneous devices are novel contributions. The objective of this work is to describe how to implement the parallel execution of LGP in order to use the underlying hardware (especially GPU) on the different platforms while still maintaining loyalty to the general methodology of the LGP algorithm built for the common framework. We discuss the implementation of texture-based data structures and the sequential and parallel algorithms built for their use on both CPU and GPU. Following the description of the general algorithm, the particular tailoring of the implementations for each hardware platform is described. Sequential (CPU) and parallel (GPU-based) algorithm performance is compared on both PC and video platforms using the metrics of GP operations per second, actual time elapsed, speedup of parallel over sequential implementation, and percentage of execution time used by the GPU versus CPU.

Water Supply Reservoir Operation by Combined Genetic Algorithm – Linear Programming (GA-LP) Approach

Multi-reservoir operation planning is a complex task involving many variables, objectives, and decisions. This paper applies a hybrid method using genetic algorithm (GA) and linear programming (LP) developed by the authors to determine operational decisions for a reservoir system over the optimization period. This method identifies part of the decision variables called cost reduction factors (CRFs) by GA and operational variables by LP. CRFs are introduced into the formulation to discourage reservoir depletion in the initial stages of the planning period. These factors are useful parameters that can be employed to determine operational decisions such as optimal releases and imports, in response to future inflow predictions. A part of the Roadford Water Supply System, UK, is used to demonstrate the performance of the GA-LP method in comparison to the RELAX algorithm. The proposed approach obtains comparable results ensuring non zero final storages in the larger reservoirs of the Roadford Hydrosystem. It shows potential for generating operating policy in the form of hegging rules without a priori imposition of their form.