Genetic Algorithm Strategy Research Articles

Optimal Resource Allocation and Interference Management for Multi-User Uplink Light Communication Systems With Angular Diversity Technology

Light communication (LC) technology has been regarded as a promising candidate for future indoor wireless networks by providing safe, power-efficient, and high data rate communications needed for tomorrow's applications. Both visible light (VL) and infrared (IR) wavelengths can be utilized to design LC systems. It is often proposed that VL can be used to offload downlink traffic while near-IR is typically used in the uplink. In this paper, the uplink multi-user LC system is considered where the system performance is degraded by both inter-symbol interference (ISI) resulting from multipath reflections and inter-user interference (IUI) coming from neighboring users. To mitigate these limitations, an optimal fair resource allocation (OFRA) scheme is proposed which aims to improve the fairness among the users in terms of their received signal to interference plus noise ratios (SINRs) by implementing the angle diversity technology. Precisely, by assigning an ON/OFF state for each LED of the angle diversity transmitter (ADT), used by each user, the IUI can be significantly reduced. Also, the angle diversity receiver (ADR) is used to effectively mitigate the effects of ISI. The allocation matrix which achieves the highest fairness between different users is obtained for different scenarios of user distribution. Toward this, the exhaustive search (ES) method is used to obtain the optimal solution for the optimization problem under consideration. However, to reduce the time complexity of ES method, a quasi-optimal solution called sub-optimal fair resource allocation scheme SFRA is proposed. The sub-optimal solution is based on the genetic algorithm (GA) scheme. The simulation results reveal that both the OFRA and SFRA achieve almost the same performance. Moreover, the simulation results indicate the superior performance of the proposed OFRA scheme over the conventional single transmitter (ST) one.

Diagnosis of Left Ventricular Hypertrophy Based on Electrocardiogram Signal and Fuzzy Logic

Sokolow – Lyon index in detection of left ventricular hypertrophy is a hard limited index, so the clinical manifestation of the disease can be ignored when the measured index is near the threshold. Several proposed studies incorporate multiple index to improve diagnostic quality. However, the process of examination and diagnosis will be longer due to the need to collect more data. To solve this problem, the paper proposes a method of classifying left ventricular hypertrophy using fuzzy logic combining with digital signal processing techniques. The proposed method mainly uses the Sokolov-Lyon index (SV1+RV5/V6 ≥ 35 mm) for major changes in ECG signal but with four soft thresholds corresponding to the different clinical manifestations of the disease. In addition, a program is written in C++ language with QT Creator compiler also is developed to implement the algorithm. From there, the doctors can refer and propose to the patient's treatment regimen.
 Keywords
 ECG, left ventricular hypertrophy, signal processing, fuzzy logic.
 References
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A New Genetic Algorithm Approach Applied to Atomic and Molecular Cluster Studies.

A new procedure is suggested to improve genetic algorithms for the prediction of structures of nanoparticles. The strategy focuses on managing the creation of new individuals by evaluating the efficiency of operators (o1, o2,…,o13) in generating well-adapted offspring. This is done by increasing the creation rate of operators with better performance and decreasing that rate for the ones which poorly fulfill the task of creating favorable new generation. Additionally, several strategies (thirteen at this level of approach) from different optimization techniques were implemented on the actual genetic algorithm. Trials were performed on the general case studies of 26 and 55-atom clusters with binding energy governed by a Lennard-Jones empirical potential with all individuals being created by each of the particular thirteen operators tested. A 18-atom carbon cluster and some polynitrogen systems were also studied within REBO potential and quantum approaches, respectively. Results show that our management strategy could avoid bad operators, keeping the overall method performance with great confidence. Moreover, amongst the operators taken from the literature and tested herein, the genetic algorithm was faster when the generation of new individuals was carried out by the twist operator, even when compared to commonly used operators such as Deaven and Ho cut-and-splice crossover. Operators typically designed for basin-hopping methodology also performed well on the proposed genetic algorithm scheme.

A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

Abstract In the aluminum reduction process, aluminum fluoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic efficiency. Making the decision on the amount of AlF3 addition (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into consideration a variety of interrelated functions; in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is difficult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have difficulty covering these complex causalities. In this work, a data and knowledge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy decision trees, which are used to amend the initial structure provided by experts. The state transition algorithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

РOSSIBILITY OF USE AND ADAPTATION OF GENETIC ALGORITHMS FOR RATIONAL DESIGN OF TOOTHED HELICAL REDUCERS AND GEARBOXES

The article is devoted to the possibility of using and adapting genetic algorithms (GA) for rational design of cylindrical reducers gearboxes. The main theory of the LP-search method are considered, which allow to evaluate the possibilities of this method and analyze the prospects of its expansion using the ideology of GA. The basic theory concerning GA are considered. The main differences between GA and classical optimization methods are given. The algorithmic scheme of classical GA is described. An analysis of the main genetic selection of parents, cross-breeding and mutations is given. An estimation of the basic genetic operators is made on their productivity and convenience of use, and determined by their choice for further work. Possible variants of modifications of GA sequences are considered considering the features of the problem of rational design of geared cylindrical gearboxes. The first one provides an algorithm that allows a significant increase in the number of viable individuals, that is, test points satisfying the numerical and functional limitations of the data, thereby offsetting the lack of limitation on the maximum possible number of test points in the LP-search. The following describes an algorithm that focuses primarily on the selection of more qualitative test points and the creation of an appropriate population that is inherent in genetic-evolutionary algorithms (GEAs). It is based on the operator - the withdrawal of less quality points, as well as the mandatory use of the operator mutation. Thus, a theoretical basis for further testing of proposed modifications of GA algorithms was created.

Generation of carrier-envelope phase-controlled isolated attosecond pulses using chirped femtosecond excitation lasers

ABSTRACTWe present an efficient approach for producing a carrier-envelope phase controlled isolated attosecond pulse by an optimized intense driving laser pulse. High-order harmonics are produced by numerically solving the time-dependent Schrödinger equation for the one-dimensional hydrogen atom in an ultrashort laser pulse. We define an efficient cost function to optimize the laser pulse by a genetic algorithm scheme. Our approach produces single attosecond pulses with desired properties, including the carrier-envelope phase, central frequency, and duration. Also, we analyze the time–frequency profiles of the attosecond emissions to gain a deeper insight into the underlying physical mechanism.

Modeling and Optimization of a Multi-Objective Ridesharing Problem in the Case of Medical Waste

Medical wastes is now a major concern of the world community and more particularly that of Moroccans. Indeed, these wastes, classified as hazardous products, are the source of serious infections, contamination of groundwater and air pollution. Through this paper, we encouraged the use of ridesharing to cope with the risks and costs arising from the logistics of these medical wastes. Thus, we have proposed a mathematical model that governs the multi-objective nature of this logistics and the various constraints associated with it. Since the exact approach had difficulties in large instances, we proposed the Genetic Algorithm and Evolution Strategy as metaheuristic to solve the model. The Evolution Strategy showed its efficiency and stability and therefore we have demonstrated through this metaheuristic the possibility of a compromise between the main objectives of our model.

A general framework of design flood estimation for cascade reservoirs in operation period

The hydrological regimes of downstream reservoirs have been significantly altered due to the operation and regulation of upstream cascade reservoirs. The original design flood quantiles, namely “design flood in construction period”, do not consider anthropogenic impacts in reservoir operation period, and have led to enormous conflicts between flood control and conservation. In this study, the “design flood and flood limited water level in operation period” are defined for practical application. We establish a general framework to measure the spatiotemporal pattern of streamflow and to estimate design floods of cascade reservoirs in operation period. The multivariate t-copula and a genetic algorithm strategy are proposed to solve the curse of dimensionality encountered in the derivation of most likely regional composition. The Jinsha River and Yalong River cascade reservoir system in China, which consists of 13 large reservoirs with the total storage capacity of 74.06 billion m3 and hydropower capacity of 71.47 GW, is selected as a case study. Results indicate that: (1) The curse of dimensionality can be well addressed by applying multivariate t-copula to build high dimensional joint distribution and using the genetic algorithm to achieve the most likely regional composition. (2) Compared with the design floods in construction period, the design floods of downstream reservoirs in operation period have been significantly reduced due to the upstream reservoir regulation. The 1000-year design peak flood discharge, 3-day, 7-day and 30-day flood volumes of Xiangjiaba reservoir decrease by 38.7%, 37.4%, 34.2% and 13.8%, respectively. (3) The flood limited water level of these reservoirs can be raised without increasing flood control risks in operation period. The cascade reservoirs in the Jinsha River and Yalong River can generate 3.28 billion kW h more hydropower (or increase 4.3%) annually during flood season.

Bat algorithm assisted by ordinal optimization for solving discrete probabilistic bicriteria optimization problems

The discrete probabilistic bicriteria optimization problem (DPBOP) is a discrete optimization problem with probabilistic criteria which should be optimized simultaneously. The DPBOP belongs to a class of NP-hard problems because the computing time increases much faster when the size of the solution space increases. To solve the DPBOP efficiently, an algorithm that used bat algorithm (BA) assisted by ordinal optimization (OO), abbreviated as BAOO, is proposed to determine an outstanding solution within an acceptable time. The BAOO algorithm comprises three parts, surrogate model, exploration and exploitation. In surrogate model, the support vector regression is utilized as a fitness evaluation of a solution. In exploration, an amended bat algorithm is adopted to select N superior solutions from the whole solution space. In exploitation, an intensified optimal computing budget allocation scheme is adopted to decide an outstanding solution from the N superior solutions. The above three parts substantially decrease the required computing overhead of DPBOP. Finally, the BAOO algorithm is applied to a facility-sizing optimization problem in factory, which is formulated as a DPBOP. Three different size problems are considered as test examples. The BAOO algorithm is compared with three general optimization methods, particle swarm optimization, genetic algorithm and evolutionary strategy. Experimental results illustrate that the BAOO algorithm yields an outstanding solution with a higher quality and efficiency than three general optimization methods.

Energy-efficient optimal task offloading in cloud networked multi-robot systems

Task offloading plays a critical role in cloud networked multi-robot systems for leveraging computation support from cloud infrastructure and benefiting greatly from the well-developed cloud network facilities. However, considering the delay constraint, the extra costs of data transmission and remote computation, it is not trivial to make optimized offloading decisions. In particular, task offloading for robots is more complex due to their on-demand mobility and network connectivity that significantly influence the robot–cloud communication links. Moreover, for multi-robot systems, a suitable balance of workload between local network (robot–robot) and global cloud (robot–cloud) is also required, so as to attain proper utilization of resources. Therefore, it is essential to establish more comprehensive offloading schemes for modeling systems that can handle these higher level of complications. With that view, this paper aims to develop a novel multi-layer decision-making scheme for task offloading which jointly considers the following four aspects: (i) selection of task for offloading, (ii) selection of robot to offload a task, (iii) selection of location to offload/perform task, (iv) selection of access point for offloaded task. An integrated framework for cloud networked multi-robot systems is presented to enable our task offloading scheme where the primary robot can aid from additional local robots to improve the offloading process. In particular, we consider a warehouse scenario with 36 cell workspace where a 40 node taskflow is motivated from a “parcel sorting and distribution” application, to be completed by the primary robot. The offloading decision for each task is formulated as part of a joint optimization problem and it is solved by developing a multi-layer genetic algorithm scheme that takes into account motion, network connectivity and local sharing for its offloading decisions. We evaluate the results of the scheme via comparison with two validated benchmarks. The outcome highlights a significant improvement in overall system performance due to joint involvement of motion (path planning), connectivity (bandwidth estimation) and robot–robot communication (local offloading) that facilitates energy-efficient offloading to cloud, faster completion of tasks and better utilization of available resources.

A hybrid genetic algorithm for the degree-constrained minimum spanning tree problem

Given an undirected, connected, edge-weighted graph G and a positive integer d, the degree-constrained minimum spanning tree (dc-MST) problem aims to find a minimum spanning tree T on G subject to the constraint that each vertex is either a leaf vertex or else has degree at most d in T, where d is a given positive integer. The dc-MST is $$\mathcal {NP}$$-hard problem for d$$\ge $$ 2 and finds several real-world applications. This paper proposes a hybrid approach ($$\mathcal {H}$$SSGA) combining a steady-state genetic algorithm and local search strategies for the this problem. An additional step (based on perturbation strategy at a regular interval of time) in the replacement strategy is applied in order to maintain diversity in the population throughout the search process. On a set of available 107 benchmark instances, computational results show the superiority of our proposed $$\mathcal {H}$$SSGA in comparison with the state-of-the-art metaheuristic techniques.

Communication-Aware Cloud Robotic Task Offloading With On-Demand Mobility for Smart Factory Maintenance

The fourth industrial revolution introduces an ideal opportunity for inclusion of cloud-enabled robots in a factory environment to improve productivity and reduce human intervention. For this novel paradigm, task offloading plays a critical role in leveraging computation support from resourceful cloud infrastructure. In particular, network connectivity and on-demand mobility of robot significantly influence the task offloading decision-making and vice-versa. While current studies in the literature separately consider mobility or communication aspects to accommodate offloading, ours is the first approach to integrate these three interdependent factors together in order to formulate a joint optimization problem for the proposed oil factory maintenance application. A modified genetic algorithm scheme is then developed to solve the problem with a novel 3-layer decision: task offloading, path planning, and access point selection. Simulation results and comparison with existing techniques suggest that communication-aware and mobility-driven offloading in industrial scenario leads to superior system performance and minimum consumption of resources.

Combining Pole Placement and Online Empirical Mode Decomposition Methods to Adaptive Active Control of Structural Vibration

Abstract Using a combination of the pole placement and online empirical mode decomposition (EMD) methods, a new algorithm is proposed for adaptive active control of structural vibration. The EMD method is a time-frequency domain analysis method that can be used for nonstationary and nonlinear problems. Combining the EMD method and Hilbert transform, which is called Hilbert–Huang transform, achieves a method that can be implemented to extract instantaneous properties of signals such as structural response dominant instantaneous frequencies. In the proposed algorithm, these estimated instantaneous properties are utilized to improve the pole-placement method as an adaptive active control technique. The required active control gains are obtained using a genetic algorithm scheme, and optimal gains are calculated corresponding to preselected excitation frequencies. An algorithm is also introduced to choose excitation frequencies based on online EMD method resolution. In order to investigate the efficiency of the proposed method, some case studies that include a discrete model, continuous samples of beam and plate structures, and experimental cantilevered beam are carried out, and the results of the proposed method are compared with the preset (nonadaptive) optimal gains conditions.

An Efficient Algorithm for Identifying Mutated Subnetworks Associated with Survival in Cancer.

Protein-protein interaction (PPI) network models interconnections between protein-encoding genes. A group of proteins that perform similar functions are often connected to each other in the PPI network. The corresponding genes form pathways or functional modules. Mutation in protein-encoding genes affect behavior of pathways. This results in initiation, progression, and severity of diseases that propagates through pathways. In this work, we integrate mutation, survival information of patients, and PPI network to identify connected subnetworks associated with survival. We define the computational problem using a fitness function called log-rank statistic to score subnetworks. Log-rank statistic compares the survival between two populations. We propose a novel method, Survival Associated Mutated Subnetwork (SAMS) that adopts genetic algorithm strategy to find the connected subnetwork within the PPI network whose mutation yields highest log-rank statistic. We test on real cancer and synthetic datasets. SAMS generate solutions in negligible time while the state-of-art method in literature takes exponential time. Log-rank statistic of SAMS selected mutated subnetworks are comparable to the method. Our result genesets show significant overlap with well-known cancer driver genes derived from curated datasets and studies in literature, display high text-mining score in terms of number of citations combined with disease-specific keywords in PubMed, and identify pathways having high biological relevance.

The accuracy and efficiency of GA and PSO optimization schemes on estimating reaction kinetic parameters of biomass pyrolysis

Reaction kinetic parameters estimation of biomass pyrolysis is a relatively difficult optimization problem due to the complexity of pyrolysis model. Two common heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are applied to estimate the kinetic parameters of three-component parallel reaction mechanism based on the thermogravimetric experiment in wide heating rates. The accuracy and efficiency of GA and PSO algorithms are compared with each other under the identical optimization conditions. The results indicate the better optimization abilities of PSO with the closer convergence solution to the global optimum and quicker convergence to the solution than GA based on the three-component parallel reaction mechanism of biomass pyrolysis. Especially, the improvement of best fitting value of PSO reaches up to 30% compared with that of GA. Furthermore, 14 estimated kinetic parameters of best fitting value are obtained and the mass loss rate predicted results including three separate components (hemicellulose, cellulose and lignin) are compared with experimental data.

An optimized non-linear input shaper for payload oscillation suppression of crane point-to-point maneuvers

Payload oscillation suppression in point-to-point maneuvers is a challenging problem in the design and control process of overhead crane systems. To address this issue, this article proposes an input shaping control method coupled with a genetic algorithm strategy to tune the jib input acceleration. A polynomial function is defined as the input for the jib acceleration to move the payload on a specific track that satisfies the system constraints, while the desired final conditions are achieved. All three phases of a crane maneuver, including filling the ladle, moving the payload to a new position, and eventually unloading the ladle, are captured by the optimization algorithm. The proposed method is evaluated for both constant and variable hoisting cable length cases. The numerical results indicate that the introduced method enables the crane to safely perform an accurate controlled maneuver by constraining the payload angular motion within a certain range, which is critical in handling sensitive loads. In compared with the existing techniques, the new input shaper also provides a faster system response owing to the fact that it is driven by a continuous input function. A sensitivity study is performed and a non-sensitive optimized input polynomial function is obtained for the variable hoisting length case.

Optimized Computational Analysis of Feedforward and Feedback Control Scheme using Genetic Algorithm Techniques

A computational analysis using Artificial Intelligence techniques has drawn its attention in Industrial Revolution 4.0. There are self-regulating and integrating processes involved. The commonly used control method involves single-loop feedback control. Embedding feedforward algorithm is aimed to improve the steady state response of the controlled process without affecting the performance of transient response. Apart of it, two optimal tunings are respectively applied to suit to the different control objectives. Correlation PID tunings are applied to respective objective somehow is very relying to the engineering experience and skills. This paper proposed using Genetic Algorithm for optimization analysis to search a trade-off optimized PI controller settings, which reduces dependency on skills as well as provide better insights to all practical engineers for commending effective PI tunings to the real control practices of plants. Initially, the respective process and disturbance models of LOOP-PRO software for both Jacketed-reactor and Pumped-tank were developed. Then, the simulation analysis of PI tunings was conducted to feedback control, feedforward and feedback and Genetic Algorithm scheme. Relative performance was compared in terms of graphs, performance index, and performance indicator. It is concluded that Genetic Algorithm has consistently provided a trade-off optimized PI controller settings for both Jacketed-reactor and Pumped-tank.

Research on facility layout optimization algorithm of deep-water semi-submersible drilling platform

We aimed to create a facility layout design of semi-submersible drilling platform (DSDP) with performance constraints. The Boltzmann survival mechanism of simulated annealing algorithm was introduced into the replacement strategy of genetic algorithm to form an improved genetic algorithm called genetic and simulated annealing algorithm (GASA). The new algorithm alleviates the “combination explosion” and premature convergence of the traditional genetic algorithm. The layout problem of DSDP is efficiently solved by using this new algorithm. When the number of layout objects increases, GASA’s performance is better than that of the genetic algorithm.

Smart adaptive run parameterization (SArP): enhancement of user manual selection of running parameters in fluid dynamic simulations using bio-inspired and machine-learning techniques

Computational fluid dynamic (CFD) simulations present numerous challenges in the domain of artificial intelligence. Computational time, resources and cost that can reach disproportional size before leading a simulation to its fully converged solution are one of the central issues in this domain. In this paper, we propose a novel algorithm that finds optimal parameter settings for the numerical solvers of CFD software. Indeed, this research proposes an alternative approach; rather than going deeper in reducing the mathematical complexity, it suggests taking advantage of the history of previous runs in order to estimate the best parameters for numerical equation resolution. In fact, our approach is bio-inspired and based on a genetic algorithm (GA) and evolutionary strategies enhanced with surrogate functions based on machine-learning meta-models. Our research method was tested on 11 different use cases using various configurations of the GA and algorithms of machine learning such as regression trees extra trees regressors and random forest regressors. Our approach has achieved better runtime performance and higher convergence quality (an improvement varying between 8 and 40%) in all of the test cases when compared to a basic approach which requires manually selecting the parameters. Moreover, our approach outperforms in some cases manual selection of parameters by reaching convergent solutions that couldn’t otherwise be achieved manually.

Time-domain Spectral Element Method for Impact Identification of Frame Structures using Enhanced GAs

This paper develops an Enhanced Genetic Algorithm (GA) strategy in conjunction with a time-domain Spectral Finite Element Method (SFEM) for impact identification of framed structures. For this purpose, a spatial truss spectral element is proposed for impact response simulation. In this regard, Gauss-Lobatto-Legendre quadrature rules and points configuration are adopted to construct a diagonal matrix and to gain an optimum computational demands. A decimal and a mixed GA coding system are implemented to locate and re-construct the impact features, respectively. An improved GA-based fitness assessment is designed to significantly accelerate the convergence rate of the optimization strategy. The impact identification of two frame structures is investigated taking into account the influence of externally applied loading. It is concluded that, the proposed mixed coding GA strategy effectively overcomes the main drawbacks of classical GA approach. The developed SFEM is superior to conventional FEM because of its high order interpolation and integration rules. For large structures, impact localization is successfully accomplished very fast, which provides the excellent ability in developing an on-line health monitoring system. It is included that, the robustness of the proposed SFEM lies on the considerably higher computational efficiency in achieving the most accurate results with the less computational costs.