The main objective of this paper is to present a hybrid technique named as a PSO-GA for solving the constrained optimization problems. In this algorithm, particle swarm optimization (PSO) operates in ...the direction of improving the vector while the genetic algorithm (GA) has been used for modifying the decision vectors using genetic operators. The balance between the exploration and exploitation abilities have been further improved by incorporating the genetic operators, namely, crossover and mutation in PSO algorithm. The constraints defined in the problem are handled with the help of the parameter-free penalty function. The experimental results of constrained optimization problems are reported and compared with the typical approaches exist in the literature. As shown, the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. Furthermore, experimental results indicate that the proposed approach may yield better solutions to engineering problems than those obtained by using current algorithms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
•A novel solar micro Combined Cooling, Heating, and Power cycle is proposed.•Parametric study is presented to investigate the effects of various parameters.•Thermodynamic and thermoeconomic ...optimizations of the desired system are conducted.•Multi-objective optimization technique is applied using Genetic Algorithm.
This paper proposes a novel micro solar Combined Cooling, Heating and Power (CCHP) cycle integrated with Organic Rankine Cycle (ORC) for summer and winter seasons. The thermal storage tank is installed to correct the mismatch between the supply of the solar energy and the demand of thermal source consumed by the CCHP subsystem, thus the desired system could continuously and stably operate. The cycle is analyzed and optimized from the viewpoint of thermodynamics and thermoeconomics. For summer mode, the thermal efficiency, exergy efficiency and product cost rate are found to be 23.66%, 9.51% and 5114.5$/year, while for winter mode, these values are 48.45%, 13.76% and 5688.1$/year, respectively. Five key parameters, namely turbine inlet temperature, turbine inlet pressure, turbine back pressure, evaporator temperature and heater outlet temperature are selected as the decision variables to examine the performance of the overall system. The thermal efficiency, exergy efficiency and total product cost rate are selected as three objective functions and Genetic Algorithm (GA) is employed to find the final solutions to both single and multi-objective optimizations of the system. The results indicate that in summer, thermal efficiency, exergy efficiency and total product cost rate in optimum case are improved to 28%, 27% and 17%, respectively, while in winter, these values are 4%, 13% and 4%.
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We present a de novo discovery of an efficient catalyst of the Morita–Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower the estimated barrier of the rate‐determining ...step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We identify 435 candidates, virtually all of which contain an azetidine N as the catalytically active site, which is discovered by the GA. Two molecules are selected for further study based on their predicted synthetic accessibility and have predicted rate‐determining barriers that are lower than that of a known catalyst. Azetidines have not been used as catalysts for the MBH reaction. One suggested azetidine is successfully synthesized and showed an eightfold increase in activity over a commonly used catalyst. We believe this is the first experimentally verified de novo discovery of an efficient catalyst using a generative model.
An efficient catalyst of the Morita–Baylis–Hillman reaction was discovered using a graph‐based genetic algorithm. The catalytic activity was experimentally verified by a kinetic study and the newly discovered catalyst outcompetes a widely used catalyst for this reaction.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Feature selection methods are used to identify and remove irrelevant and redundant attributes from the original feature vector that do not have much contribution to enhance the performance of a ...predictive model. Meta-heuristic feature selection algorithms, used as a solution to this problem, need to have a good trade-off between exploitation and exploration of the search space. Genetic Algorithm (GA), a popular meta-heuristic algorithm, lacks exploitation capability, which in turn affects the local search ability of the algorithm. Basically, GA uses mutation operation to take care of exploitation which has certain limitations. As a result, GA gets stuck in local optima. To encounter this problem, in the present work, we have intelligently blended the Great Deluge Algorithm (GDA), a local search algorithm, with GA. Here GDA is used in place of mutation operation of the GA. Application of GDA yields a high degree of exploitation through the use of perturbation of candidate solutions. The proposed method is named as Deluge based Genetic Algorithm (DGA). We have applied the DGA on 15 publicly available standard datasets taken from the UCI dataset repository. To show the classifier independent nature of the proposed feature selection method, we have used 3 different classifiers namely K-Nearest Neighbour (KNN), Multi-layer Perceptron (MLP) and Support Vector Machine (SVM). Comparison of DGA has been performed with other contemporary algorithms like the basic version of GA, Particle Swarm Optimisation (PSO), Simulated Annealing (SA) and Histogram based Multi-Objective GA (HMOGA). From the comparison results, it has been observed that DGA performs much better than others in most of the cases. Thus, our main contributions in this paper are introduction of a new variant of GA for FS which uses GDA to strengthen its exploitational ability and application of the proposed method on 15 well-known UCI datasets using KNN, MLP and SVM classifiers.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In this paper, based on the lithium-ion battery parameter identification by Immune Genetic Algorithm, An Extended Kalman Particle Filter approach is proposed to estimate the state of charge. Immune ...Genetic Algorithm was designed to identify the second-order equivalent circuit model parameters of lithium-ion battery. Combining Extended Kalman Filter with Particle Filter, Extended Kalman Particle Filter is designed to estimate the lithium-ion battery state of charge. This method is especially for the nonlinear and time variant lithium-ion battery system, and it can improve the calculation accuracy and stability of State of Charge estimation. An Immune Genetic Extended Kalman Particle Filter approach is validated by some experimental scenarios on the test bench. Experimental results show that Immune Genetic Extended Kalman Particle Filter has better adaptability, robustness and accuracy than Extended Kalman Filter under both UDDS and ECE conditions. Both theoretical and experimental results illustrate that Extended Kalman Particle Filter is a good candidate to estimate the lithium-ion battery state of charge.
•An immune genetic algorithm is designed to be firstly applied into paraments identification of lithium-ion battery model.•A noise variance estimation algorithm is firstly utilized to improve extended Kalman particle filter.•An immune-genetic-based extended Kalman particle filter approach is developed to estimate SOC for lithium-ion battery.•The IG-EKPF approach is a good candidate of SOC estimation based on the validation of engineering practice.
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Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological ...services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. Finally, comprehensive experimental analysis of DRPM are proceeded.
A Genetic Algorithm for a Green Vehicle Routing Problem de Oliveira da Costa, Paulo Roberto; Mauceri, Stefano; Carroll, Paula ...
Electronic notes in discrete mathematics,
February 2018, 2018-02-00, Volume:
64
Journal Article
Open access
We propose a Genetic Algorithm (GA) to address a Green Vehicle Routing Problem (G-VRP). Unlike classic formulations of the VRP, this study aims to minimise the CO2 emissions per route. The G-VRP is ...of interest to policy makers who wish to reduce greenhouse gas emissions. The GA is tested on a suite of benchmark, and real-world instances which include road speed and gradient data. Our solution approach incorporates elements of local and population search heuristics. Solutions are compared with routes currently used by drivers in a courier company. Reductions in emissions are achieved without incurring additional operational costs.
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Scheduling is an important decision-making problem in production planning and the resulting decisions have a direct impact on reducing waste, including energy and idle capacity. Batch scheduling ...problems occur in various industries from automotive to food and energy. This paper introduces the parallel p-batch scheduling problem with batch delivery, content-dependent loading/unloading times and energy-aware objective function. The problem has been motivated by a real system used for freezing products in a food processing company. A mixed-integer linear programming model (MILP) has been developed and explained through a numerical example. As it is not practical to solve large-size instances via a mathematical model, the discrete differential evolution algorithm has been improved (iDDE) and hybridised with the genetic algorithm (GA). A release-oriented vector generation procedure and a heuristic batch formation mechanism have been developed to efficiently solve the problem. The performance of the proposed approach (iDDEGA) has been compared with CPLEX, iDDE and GA through a comprehensive computational study. A case study was conducted based on real data collected from the freezing process of the company, which also verified the practical use and advantages of the proposed methodology.
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Current courier networks in metropolitan areas are characterised by utilising fleets of vans that perform collection and distribution routes independently. This results in long stem distances, low ...load factors and high environmental costs. Joint delivery systems have the potential to reduce the distances of pick-up and drop-off routes. In this context, parcel lockers can be utilised to transfer goods between vans, electrical vehicles and bikes to improve the efficiency and sustainability of courier networks. This paper presents a model for designing joint delivery networks in urban areas by utilising parcel lockers. This model has a two-level structure: the lower level dealing with multi-depot capacitated vehicle routing problems (MDCVRP) for a set of depots and lockers whilst the upper level being a (minimum-cost) parcel network flow problem (PNFP) considering goods delivered between depots and lockers and the selection of lockers' positions and sizes. A hybrid algorithm integrating a Genetic Algorithm with the Lin-Kernighan Heuristic has been developed. The GA focuses on finding solutions for the PNFP. Once the paths of parcel flow are determined, the LKH optimises vehicle flow. This paper is the first to consider the use of parcel lockers for business-to-business networks in the form of MDCVRP.
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Materials informatics employs machine learning (ML) models to map the relationship between a targeted property and various materials descriptors, providing new avenues to accelerate ...the discovery of new materials. However, the possible ML models and materials descriptors are numerous, and a rational recipe to rapidly choose the best combination of the two is needed. In the present study, we propose a systematic framework that utilizes a genetic algorithm (GA) to efficiently select the ML model and materials descriptors from a huge number of alternatives and demonstrated its efficiency on two phase formation problems in high entropy alloys (HEAs). The optimized classification model allows an accuracy for identifying solid-solution and non-solid-solution HEAs to be up to 88.7% and further for recognizing body-centered-cubic (BCC), face-centered-cubic (FCC), and dual-phase HEAs to reach 91.3%. Furthermore, by employing an active learning approach, several HEAs with largest classification uncertainties were selected, experimentally synthesized and phase-identified, and augmented to the initial dataset to iteratively improve the ML model. The method serves as a general algorithm to select materials descriptors and ML models for various material problems including classification and optimization of targeted properties.
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