Basic Genetic Algorithm Research Articles

A mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles

This paper presents a new mathematical model for location of temporary relief centers and dynamic routing of aerial rescue vehicles distributing basic supplies in relief operations. The objective function of the proposed model minimizes the arrival time at the last designated temporary relief center. The proposed model seeks to locate the temporary relief centers in a way that all affected areas get covered by at least one temporary relief center. Considering the importance of quick action amid a post-disaster environment with characteristics such as uncertain demand for relief supplies, inaccurate information regarding victims, aftershocks, and extensively damaged road networks, in the proposed model, location and level of demand are considered to be dynamic, and relief supplies are assumed to be distributed by aerial transport vehicles. According to NP-Hardness of proposed problem, in this paper an improved hybrid metaheuristic algorithm based on scatter search combined with variable neighborhood search is presented. To evaluate the performance of the proposed algorithm, it is tested and compared with an exact method, basic scatter search and genetic algorithm. Results show the good performance of the proposed algorithm.

Application on Cold Chain Logistics Routing Optimization Based on Improved Genetic Algorithm

As the rise of fresh e-supplier, cold chain logistic has become the hot topics in China. But due to its special timeliness, it is necessary to optimize its vehicle routing. Firstly, we construct a cold chain logistics vehicle routing optimization with soft time windows model. Secondly, as simple genetic algorithm has some shortcomings such as poor population diversity and slow convergence, we propose an improved genetic algorithm – seeker genetic algorithm. By combining the uncertainty reasoning behavior in the seeker optimization algorithm and the nearest neighbor strategy, we improve the mutation operator in the genetic algorithm. Finally, we solve the cold chain logistics vehicle routing optimization model with basic genetic algorithm and seeker genetic algorithm respectively. The results indicate that seeker genetic algorithm could find the path with lower cost.

Discrimination of polycyclic aromatic hydrocarbons based on fluorescence spectrometry coupled with CS-SVM

A new approach for discrimination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on fluorescence coupled with CS-SVM. Two groups of experiments were carried out on PAHs with similar spectra. The first one was PAHs any two among benzo[k]fluoranthene (BkF), benzo[b]fluoranthene (BbF) and benzo[a]pyrene (BaP). The second one was naphthalene (NAP) and fluorene (FLU) with similar spectra. The BaP-BkF mixture, BaP-BbF mixture and BbF-BkF mixture with similar fluorescence properties, cuckoo search algorithm (CS) optimizing support vector machine (SVM) was used to discriminate these three mixtures. By comparison with the basic grid search algorithm (GS), genetic algorithm (GA) and particle swarm optimization algorithm (PSO) optimizing SVM, it was found that fitting degree of CS was the best and the convergence speed was also the fastest. The test sample classification accuracy of CS-SVM can reach 100%, which was higher than that of GS-SVM, GA-SVM and PSO-SVM. In order to verify the validity of the proposed approach, all the above methods were applied to discriminate NAP and FLU with extremely similar spectra. The test sample classification accuracy can reach 100%. The satisfying results indicated that the proposed approach had potential to be an alternative approach for discriminating PAHs in environment.

A Heuristic Genetic Algorithm for Regional Targets’ Small Satellite Image Downlink Scheduling Problem

Small satellite image downlink scheduling problem (SSIDSP) is an important part of satellite mission planning. SSIDSP mainly needs to balance how to better match the limited receiving capacity of the ground station with the limited satellite resources. In this paper, regional targets are considered with SSIDSP. We propose a mathematical model that maximizes profit by considering time value and regional targets. A downlink schedule algorithm (DSA) is proposed to complete the task sequence arrangement and generate scheduling results. A heuristic genetic algorithm (HGA) is used to optimize the generated task sequence to achieve higher profit. Three scale test instances are used to test the effectiveness of HGA and DSA. We compare the effect of HGA, basic genetic algorithm (GA), and construction heuristic algorithm. The experimental results proved that the proposed approach ensures the successful completion of observation tasks and is effective for SSIDSP.

Reliable S-Box Hardware Implementation by Gate-Level Fault Masking Enhancement

With technology scaling, fault tolerance has become more essential for digital circuits. Some solutions, like all types of redundancies, have been proposed to increase the reliability of the systems. In this paper, we present a cost-aware algorithm to enhance the fault tolerance ability of combinational digital circuits. Proposed algorithm improves the circuit logical masking with minimum area overhead based on an improved version of genetic algorithm (GA). Given a set of potential gates that are more sensitive to fault occurrence, we first extract feasible functional redundant, ffr, between the source nodes and the potential gates’ outputs that their improvement on logical masking be more than a pre-defined threshold and hold them in a library, Masking_Lib. Then, we have to find a set of minimum number of potential gates as a target to add appropriate ffr, so that the maximum improvement on logical masking with minimum area overhead is achieved. Since, finding a set of potential gates with their suitable ffrs to meet these objectives is an NP-hard problem, we formulize this, as an optimization problem and solve using GA. We introduce an efficient chromosome representation and an adaptive objective function along with the basic GA operators. Besides, we integrate an assimilation operator with GA in order to enhance its searching ability. Our approach is applied to composite field substitution box implementation (S-box) that forms the core building block of any hardware implementation of the Advanced Encryption Standard algorithm. The simulation and synthesis results have been reported to show the effectiveness of our approach. Through these results, it has been shown that our proposed algorithm provides reliable digital circuits based on different level of logical masking (from 25.58 to 52.15).

Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy

This paper presents an improved genetic algorithm using novel crossover and mutation (IGA-NCM) to solve the combined heat and power economic dispatch (CHPED) problems. The basic genetic algorithm (GA) has been augmented in three aspects. First, the selection operation is excluded from GA in order to avoid excessive losses of population diversity. Second, two kinds of adaptive crossover operations are used to sufficiently excavate the information of parents and yield potential offsprings. Third, a novel mutation operation is used to replace a few genes of each crossed offspring by those of the other crossed offsprings’ parents, which can further improve their quality. Furthermore, a new constraint handling method is proposed to repair the mutated offsprings and enable them to enter feasible regions easily. Experimental results show that our proposed IGA-NCM algorithm outperforms the other ones according to computation accuracy and runtime. Therefore, it is a potential alternative for the CHPED problems with or without prohibited operating zones.

Bio-inspired metaheuristics for the generalised discrete cost multicommodity network design problem

We investigate a new variant of network design problems (NDPs) called the generalised discrete cost multicommodity network design problem (GDCMNDP) that arises in a wide variety of real-life situations such as transportation, telecommunication and logistics. The problem consists on identifying the optimal capacitated network by choosing the connections to be installed in order to satisfy partially or totally the multicommodity demands. The objective is to minimise the sum of installation costs and penalty costs due to the unrouted demands. For the GDCMNDP, we propose three basic greedy heuristics and three bio-inspired metaheuristics: a basic genetic algorithm, a hybrid genetic algorithm via a variable neighbourhood search procedure and a biogeography-based optimisation heuristic. To assess the performance of the proposed approaches, computational results are reported using real-world and benchmark instances from the literature. Computational results show that our hybrid genetic algorithm performs well by obtaining very good final solutions in reasonable times.

An improved genetic algorithm with variable neighborhood search to solve the assembly line balancing problem

Purpose This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision-makers aim to design an efficient assembly line while satisfying a set of constraints. Design/methodology/approach An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP to optimize the number of stations and the workload smoothness. Findings To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs. Originality/value The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is used in the IGA to enhance its local search capability.

Efficient job scheduling in cloud computing based on genetic algorithm

Scheduling in cloud is one of the challenging issues in resource management topic where the main question is how to manage time and cost in an optimised way. This study tackles the mentioned problem by managing time and cost through a genetic-based algorithm. The primary goal of this study is to manage jobs in a shorter time with lower cost and higher utilisation. Toward that end, we leverage the genetic algorithm solutions and a new model is proposed where jobs are created in genetic format. In the evaluation part of the model, different scenarios based on taking different fitness functions and format of the population are considered. We have analysed makespan, cost and utilisation in comparison to other two existing scheduling models (MAX-MIN and MIN-MIN). The results show considerable improvement in the cost, makespan and utilisation.

Traffic Similarity Observation Using a Genetic Algorithm and Clustering

This article presents a technique of traffic similarity observation based on the statistical method of survival analysis by using a genetic algorithm. The basis comes from the k-means clustering algorithm. The observed traffic is collected from different network sources by using a NetFlow collector. The purpose of this technique is to propose a process of finding spread malicious traffic, e.g., ransomware, and considers the possibility of implementing a genetic-based algorithm. In our solution, a chromosome is created from clustering k-means centers, and the Davies–Bouldin validity index is used as the second fitness value in the solution.

A Joint Unsupervised Learning and Genetic Algorithm Approach for Topology Control in Energy-Efficient Ultra-Dense Wireless Sensor Networks

Energy efficiency is a key performance metric for ultra-dense wireless sensor networks. In this letter, an unsupervised learning approach for topology control is proposed to prolong the lifetime of ultra-dense wireless sensor networks by balancing energy consumption. By encoding sensors as genes according to the network clusters, the proposed genetic-based algorithm learns an optimum chromosome to construct a close-to-optimum network topology using unsupervised learning in probability. Moreover, it schedules some of the cluster members to sleep to conserve the node energy using geographically adaptive fidelity. Simulation results demonstrate the superior performance of the proposed algorithm by improving energy efficiency in comparison with the state-of-the-art algorithms at an acceptable computational complexity.

Research on manufacturing productivity based on improved genetic algorithms under internet information technology

SummaryThe manufacturing characteristics of the manufacturing industry make the processing process more complex and difficult to control. How to have more effective production management and scheduling is the key link for enterprises to ensure balanced production. The problem of production scheduling and improvement of production efficiency is one of the core contents of manufacturing production management. It plays an important role in optimizing resource utilization, saving production costs and improving work efficiency. First, combined with the production characteristics of the manufacturing industry, the preliminary search ability for the basic genetic algorithm (GA) is high. The characteristics of poor search ability and strong ability of global optimization are in the late period, and the Particle Swarm Optimization (PSO) has the characteristics of poor early search ability, strong late search ability, and easy to fall into local optimum. In order to obtain better scheduling results, we make the two algorithms complementary to each other, and propose an improved genetic algorithm to solve the manufacturing efficiency problem. It is verified that the algorithm has better search performance. Second, aiming at the dynamic characteristics of manufacturing in manufacturing and processing, using the multi‐module technology of Internet information technology, the Internet information technology production planning model was constructed, and the functions, structural models, and interactive of each module were studied. Finally, the shortest path is solved by optimizing the genetic algorithm. Experiments show that, based on the improved genetic algorithm with rapid convergence, it can effectively improve the manufacturing efficiency.

Research on intelligence analysis technology of financial industry data based on genetic algorithm

Based on the improved genetic algorithm and the BP neural network, the financial data analysis model is built on the basis of the data research problem in the financial industry, especially the stock market economy. Genetic algorithm (GA) is a bionic algorithm that simulates the evolutionary mechanism of “natural selection and survival of the fittest.” Through a suitable fitness function, it enables the high-quality individuals to inherit the next generation with a larger probability. Using this method to select variables, we can optimize the variables that affect the stock price and effectively solve the selection problem of the input layer variables of the neural network. On the basis of genetic algorithm, a new GA-BP model based on genetic algorithm and neural network is proposed, and it is applied to the prediction and analysis of securities price or trend in the securities intelligence analysis system. By selecting the Shanghai and Shenzhen 300 index data, the model based on genetic algorithm is used to carry out the experiment, and the experimental results show that the method can effectively reduce the number of variables while ensuring the accuracy of the prediction.

An Improved Particle Swarm Optimization Algorithm for Integrated Scheduling Model in AGV-Served Manufacturing Systems

To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.

Urgent task-aware cloud manufacturing service composition using two-stage biogeography-based optimisation

ABSTRACTOptimal service composition is a critical issue in cloud manufacturing systems. However, unpredictable dynamic factors, such as urgent task request occurring during the production process will have an influence on the original execution plan. In this paper, a new multi-objective optimisation problem based on quality of service of urgent task-aware cloud manufacturing service composition is presented, and two service recomposition methods based on vertical collaboration and speed selection are proposed to expedite task completion. A two-stage (i.e. composition and recomposition) biogeography-based optimisation (TBBO) algorithm is proposed to solve the corresponding model. Variable neighbourhood search is used to improve the exploitation ability of the TBBO algorithm. Experiment results demonstrate that the proposed TBBO algorithm can obtain better cloud manufacturing service composition and recomposition solutions compared with the basic biogeography-based optimisation algorithm, genetic algorithm, and differential evolution, and the two proposed service recomposition methods can reduce task execution time more effectively than the original composition method.

A Genetic-Based Adaptive Approach for Reliable and Efficient Service Composition

Composite services often run in dynamically changing environments where new composition requirements can be added and Web services may disappear or their quality of service may decline. The service composition requirements are classified into three groups: functional, nonfunctional, and transactional requirements. The functional computing requirements concern functional capabilities of Web services. The nonfunctional computing requirements are defined as service-level agreements (SLA) constraints. The transactional computing requirements define behavioral constraints and are expressed by designers using the acceptable termination states (ATS) concept. In this work, we proposed an adaptive service composition (ASC) approach. Such an approach is adaptive as it can be used to either define or reconfigure composite services according to SLA and ATS constraints. It introduces a new recovery capability (RC) concept for capturing the recoverability level supported by Web services. We adopted a genetic-based algorithm to model the ASC approach. The choice of such an algorithm is justified by the nature of the problem of our ASC process which belongs to the NP-hard class. We experimentally showed the effectiveness of our ASC approach and empirically studied the service composition RC in particular.

Optimization of multiple heat releases in pre-mixed diesel engine combustion for high thermal efficiency and low combustion noise by a genetic-based algorithm method

The reduction of diesel combustion noise by multiple fuel injections maintaining high indicated thermal efficiency is an object of the research reported in this article. There are two aspects of multiple fuel injection effects on combustion noise reduction. One is the reduction of the maximum rate of pressure rise in each combustion, and the other is the noise reduction effects by the noise canceling spike combustion. The engine employed in the simulations and experiments is a supercharged, single-cylinder direct-injection diesel engine, with a high pressure common rail fuel injection system. Simulations to calculate the combustion noise and indicated thermal efficiency from the approximated heat release by Wiebe functions were developed. In two-stage high temperature heat release combustion, the combustion noise can be reduced; however, the combustion noise in amplification frequencies must be reduced to achieve further combustion noise reduction, and an additional heat release was added ahead of the two-stage high temperature heat release combustion in Test 1. The simulations of the resulting three-stage high temperature heat release combustion were conducted by changing the heating value of the first heat release. In Test 2 where the optimum heat release shape for low combustion noise and high indicated thermal efficiency was investigated and the role of each of the heat releases in the three-stage high temperature heat release combustion was discussed. In Test 3, a genetic-based algorithm method was introduced to avoid the time-consuming loss and great care in preparing the calculations in Test 2, and the optimum heat release shape and frequency characteristics for combustion noise by the genetic-based algorithm method were speedily calculated. The heat release occurs after the top dead center, and the indicated thermal efficiency and overall combustion noise were 50.5% and 86.4 dBA, respectively. Furthermore, the optimum number of fuel injections and heat release shape of multiple fuel injections to achieve lower combustion noise while maintaining the higher indicated thermal efficiency were calculated in Test 4. The results suggest that the constant pressure combustion after the top dead center by multiple fuel injections is the better way to lower combustion noise; however, the excess fuel injected leads to a lower indicated thermal efficiency because the degree of constant volume becomes deteriorates.

Multi-period project portfolio selection under risk considerations and stochastic income

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

A new insight into the schema survival after crossover and mutation for genetic algorithms having distributed population set

Genetic algorithms are one of the most popular optimization algorithms. Schema theory provides a mathematical foundation for the working of genetic algorithm. Different variants of the basic genetic algorithm have been proposed; and genetic algorithm having distributed population set (Island model of genetic algorithm) is one of them. In this paper, traditional schema theory is extended for the case of genetic algorithm having distributed population set and the probability of schema survival for such case is derived.

Parameters identification of photovoltaic solar cells and module using the genetic algorithm with convex combination crossover

ABSTRACTTo design a high-performance photovoltaic (PV) system, the parameters extraction of solar cell models is exceedingly crucial. A new variant of the genetic algorithm (GA) called Genetic Algorithm with Convex Combination Crossover (GACCC) is proposed to identify the unknown electrical parameters of different solar cell models, i.e. single diode, double diode, and PV module. GACCC is achieved by integrating a new crossover operation to maintain a good balance between the intensification of the best solutions and the diversification of the search space. To test the proposed GACCC, we have compared it to the basic GA and with other literature techniques. The results indicate a high performance of developed approach GACCC and a high accuracy of estimated parameters. In addition, the efficiency of the results is confirmed by the good agreement between the experimental I-V data and the simulated results in all cases.