Evolutionary Programming Research Articles

A New Approach of DNR by using Multi-Population Evolutionary Programming (MPEP) for Losses Minimization

A New Approach of DNR by using Multi-Population Evolutionary Programming (MPEP) for Losses Minimization

Chaotic immune symbiotic organisms search for SVC installation in voltage security control

<span>Parallel with the urbanization of the world, energy demand in the world also increased. The increase in energy demand will require a power system to be operated near its stability limit. To mitigate the problem, Flexible Alternating Current Transmission System (FACTS) devices can be installed as a compensation scheme to improve voltage security in a power system. For an effective compensation, FACTS devices should be optimally allocated in a power system. Although optimization techniques can be implemented to optimally allocate these devices, problems have been reported which would affect the performance of the optimization techniques in terms of producing high quality solutions. This paper presents the implementation of Chaotic Immune Symbiotic Organisms Search for solving optimal Static VAr Compensator (SVC) allocation problem for voltage security control. The optimization is validated in IEEE 26-Bus Reliability Test System (RTS) realizes the capability of CISOS in solving the optimization problem. Comparative studies with respect to Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) resulting in good agreement on the results and demonstrated superior performance of CISOS. Results of the study can be beneficial to power system community in terms of compensation planning prior to real world implementation.</span>

Economic load dispatch of renewable energy-based power systems with high penetration of large-scale hydropower station based on multi-agent glowworm swarm optimization

In this study, we propose a multi-agent glowworm swarm optimization (MAGSO) algorithm for the economic load dispatch (ELD) problem of a large-scale hydropower station. The MAGSO integrates the idea of the evolution of glowworm swarm optimization (GSO) and the interindividual cooperation of multi-agent system (MAS). In this structure, each glowworm of the GSO is treated as an agent cooperating in the MAS before entering the evolutionary process. The proposed mechanisms enable the algorithm to escape from local extremum, capture the optimal solution and expand the diversity of solution space. The test results of four benchmark functions verify the advantages of the algorithm in global search, convergence speed and avoiding prematurity. In comparison of two case studies with the real genetic algorithm (RGA), the evolution programming (EP), the particle swarm optimization (PSO), the ant colony optimization (ACO), and the standard glowworm swarm optimization (GSO), the MAGSO provides significantly robust solutions of ELD problem of a large-scale hydropower station.

Bioinformatics Structural and Phylogenetic Characterization of Entamoeba histolytica Alcohol Dehydrogenase 2 (EhADH2).

An amitochondriate parasite, Entamoeba histolytica, has a bifunctional ADHE enzyme (EhADH2) that contains separate acetaldehyde (ALDH) and alcohol (ADH) dehydrogenase activities. In a cluster of 25 bifunctional enzymes of single cell eukaryotes and bacteria, we present a phylogenetic analysis that suggests a lateral gene transfer event (prokaryotic ancestor to single-cell eukaryotic ancestor) and a complex structure that aligns with key homologs in the ADHE evolutionary history based on their similarity with bacterial alcohol dehydrogenases. We show that the ADHE in Entamoeba lineage diverged independently but shows significant similarities to the structure of ADHE in Fusobacterium, and a complex model that maps its ALDH and ADH domain well with bacteria such as Geobaccillus thermoglucosidasius. Our analyses likely support a lateral acquisition of an EhADH2-like ancestral gene from bacteria. Several evolutionary analyses software programs reveal that the enzyme structure is highly conserved, and maintains a similar function within a diverse set of pathogens, including Escherichia coli and Clostridium spp.

Embedded adaptive mutation evolutionary programming for distributed generation management

<span>Distribution generation (DG) is a widely used term to describe additional supply to a power system network. Normally, DG is installed in distribution network because of its small capacity of power. Number of DGs connected to distribution system has been increasing rapidly as the world heading to increase their dependency on renewable energy sources. In order to handle this high penetration of DGs into distribution network, it is crucial to place the DGs at optimal location with optimal size of output. This paper presents the implementation of Embedded Adaptive Mutation Evolutionary Programming technique to find optimal location and sizing of DGs in distribution network with the objective of minimizing real power loss. 69-Bus distribution system is used as the test system for this implementation. From the presented case studies, it is found that the proposed embedded optimization technique successfully determined the optimal location and size of DG units to be installed in the distribution network so that the real power loss is reduced.</span>

Hope: Simulation of Evolutionary Programming

Hope: Simulation of Evolutionary Programming

Synthesizing 64 Bit Architectures and E-Business using SULA

Read-write models and cache coherence have garnered great interest from both biologists and system administrators in the last several years. Given the current status of wearable algorithms, steganographers ur- gently desire the exploration of fiber-optic cables, which embodies the essential principles of e-voting technology. We argue that despite the fact that congestion control and evolutionary programming [29] can connect to realize this intent, e-business and linked lists can interact to fix this quandary.

Synthesizing Multicast Solutions using Heterogeneous Information

The producer-consumer problem and online algorithms, while natural in theory, have not until recently been considered extensive. In this position paper, we confirm the development of evolutionary programming, which embodies the practical principles of operating systems. In our research we propose new secure theory (CHIT), proving that semaphores can be made “smart”, stochastic, and classical.

Hash Tables Considered Harmful

Many system administrators would agree that, had it not been for access points, the study of evolutionary programming might never have oc-curred. In this paper, we disconfirm the analysis of the Internet, which embodies the significant principles of machine learning. We introduce an analysis of flip-flop gates, which we call Wyd-Humorism.

Multi-region dynamic economic dispatch of solar–wind–hydro–thermal power system incorporating pumped hydro energy storage

This paper suggests chaotic fast convergence evolutionary programming (CFCEP) for solving multi-region dynamic economic dispatch (MRDED) problem, multi-region economic dispatch (MRED) problem and economic dispatch (ED) problem. MRDED problem is based on multi-reservoir cascaded hydro plant with time delay, thermal plants with nonsmooth fuel cost function, wind and solar power units with uncertainty and pumped hydro energy storage. MRED problem deals with tie line constraints, transmission losses, valve point effect and proscribed workable area of thermal generators. ED problem deals with valve point effect, proscribed workable area and ramp rate limits of thermal generators. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). The efficacy of the proposed technique has been verified on convoluted three-area system for MRDED problem, four-area system for MRED problem and 140-unit Korean system for ED problem. Test results acquired from the suggested CFCEP technique have been fit to that acquired from FCEP, differential evolution (DE) and particle swarm optimization (PSO). It has been observed from the comparison that the recommended CFCEP technique has the capability to bestow with better-quality solution.

The naked mole-rat algorithm

This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm .

A Sponsor Incentive Attack Scheme for Feeder Automation Systems

The rapid deployment of intelligent electronic devices and the development of information and communication technology have improved distribution network (DN) reliability with feeder automation. Thus, the operation of DNs has become more dependent on the cyber-physical components. As the cyber security of smart grid has drawn increasing attention in recent years, this paper proposes a simple yet powerful type of attack targeting the remote terminal units. The proposed attack policy can manipulate the feeder automation operation with the aim of cutting off important power consumers. Regarding the realistic cyber-attacks that previously occurred, an optimal attack model is established for analyzing the attack policy. This optimization model aims to minimize the attack cost and the penalty of being caught or detected while maximizing the remuneration. A Bayesian attack graph model is adopted to quantify the successful probability of exploiting known and zero-day vulnerabilities. The probability of a cyber-attack being caught or detected is modeled based on search theory. Next, an enhanced self-adaptive evolutionary programming is developed to achieve satisfactory solutions for practical applications. Finally, the proposed model and corresponding solution strategies are verified using the RBTS bus 2 DN and the modified IEEE 123-node test feeder.

Integrated monte carlo-evolutionary programming technique for distributed generation studies in distribution system

This paper presents the optimal multiple distributed generations (MDGs) installation for improving the voltage profile and minimizing power losses of distribution system using the integrated monte-carlo evolutionary programming (EP). EP was used as the optimization technique while monte carlo simulation is used to find the random number of locations of MDGs. This involved the testing of the proposed technique on IEEE 69-bus distribution test system. It is found that the proposed approach successfully solved the MDGs installation problem by reducing the power losses and improving the minimum voltage of the distribution system.

Dynamic economic dispatch with demand-side management incorporating renewable energy sources and pumped hydroelectric energy storage

This paper recommends chaotic fast convergence evolutionary programming (CFCEP) for solving real-world dynamic economic dispatch (DED) with demand-side management (DSM) incorporating renewable energy sources and pumped-storage hydroelectric unit. Here, solar–wind–thermal energy system has been considered taking into account pumped-storage hydroelectric unit and uncertainty of solar and wind energy sources. DSM programs reduce cost and boost up power system security. To investigate the upshot of DSM, the DED problem is solved with and without DSM. In the recommended technique, chaotic sequences have been applied for acquiring the dynamic scaling factor setting in FCEP. The efficiency of the recommended technique is revealed on two test systems. Simulation outcomes of the suggested technique have been matched against those acquired by fast convergence evolutionary programming (FCEP), colonial competitive differential evolution and heterogeneous strategy particle swarm optimization. It has been observed from the comparison that the recommended CFCEP technique has the ability to give better-quality solution.

Distillation Fiber-Optic Cables and Journaling Dossier Systems

Internet QoS [24, 45] and journaling file sys- tems, while unfortunate in theory, have not until recently been considered extensive. After years of robust research into expert systems [3], we argue the visualization of evolutionary programming, which embodies the confusing principles of cryptoanalysis. We construct an analysis of I/O automata, which we call KopJeer

Embedded Flower Pollination Evolutionary Programming Based Technique for Voltage Stability Enhancement with Distributed Generation Installation

Embedded Flower Pollination Evolutionary Programming Based Technique for Voltage Stability Enhancement with Distributed Generation Installation

Evolutionary Programming Based Technique for Plug-in-Hybrid Electric Vehicle Charging System

Evolutionary Programming Based Technique for Plug-in-Hybrid Electric Vehicle Charging System

Combined heat and power dynamic economic dispatch with demand side management incorporating renewable energy sources and pumped hydro energy storage

This study suggests chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation for solving intricate actual world combined heat and power dynamic economic dispatch (CHPDED) problem with demand side management (DSM) incorporating renewable energy sources and pumped-storage-hydraulic unit. The valve point effect and proscribed workable area of thermal generators and solar and wind power uncertainty have been pondered. DSM programmes decrease cost and boost up power system security. To investigate the upshot of DSM, the CHPDED problem is solved with and without DSM. In the recommended CFCEP technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). Introduction of chaotic sequences helps FCEP to avoid premature convergence. The efficiency of the recommended technique is revealed in a test system. Simulation outcomes of the recommended technique have been evaluated with those attained by FCEP and differential evolution. It has been examined from the assessment that the recommended CFCEP has the capability to bestow with a better-quality solution.

Dynamic economic dispatch incorporating renewable energy sources and pumped hydroenergy storage

Due to mounting infiltration of solar and wind energy sources, it becomes essential to investigate its brunt on the dynamic economic dispatch. Here, solar–wind–thermal system integrating pumped-storage hydraulic unit has been considered. This work recommends chaotic fast convergence evolutionary programming (CFCEP) rooted in Tent equation for solving dynamic economic dispatch problem incorporating renewable energy sources and pumped-storage hydraulic unit. Chaotic sequences increase the exploitation ability in the searching space and enhance the convergence property. In the recommended technique, chaotic sequences have been pertained for acquiring the dynamic scaling factor setting in fast convergence evolutionary programming (FCEP). The efficiency of the recommended technique is revealed on two test systems. Simulation outcomes of the suggested technique have been matched up to those acquired by FCEP, differential evolution and particle swarm optimization. It has been observed from the comparison that the recommended CFCEP technique has the capability to confer with better quality solution.

Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization

In this study proposes an evaluation of different computational intelligences, i.e Fast-Evolutionary Algorithm (FEP), Firefly Algorithm (FA) and Mutate-Cuckoo Search Algorithm (MCSA) for solving single-objective optimization problem. FEP and MCSA are based on the conventional Evolutionary Programming (EP) and Cuckoo Search Algorithm (CSA) with modifications and adjustment to boost up their search ability. In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. The results showed that MCSA is better compare with FEP and FA in term of fitness value while FEP is fastest algorithm in term of computational time compare with other two algorithms.