The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential ...evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Worldwide cardiovascular diseases such as stroke and heart disease are the leading cause of mortality. While guidewire/catheter-based minimally invasive surgery is used to treat a variety of ...cardiovascular disorders, existing passive guidewires and catheters suffer from several limitations such as low steerability and vessel access through complex geometry of vasculatures and imaging-related accumulation of radiation to both patients and operating surgeons. To address these limitations, magnetic soft continuum robots (MSCRs) in the form of magnetic field-controllable elastomeric fibers have recently demonstrated enhanced steerability under remotely applied magnetic fields. While the steerability of an MSCR largely relies on its workspace-the set of attainable points by its end effector-existing MSCRs based on embedding permanent magnets or uniformly dispersing magnetic particles in polymer matrices still cannot give optimal workspaces. The design and optimization of MSCRs have been challenging because of the lack of efficient tools. Here, we report a systematic set of model-based evolutionary design, fabrication, and experimental validation of an MSCR with a counterintuitive nonuniform distribution of magnetic particles to achieve an unprecedented workspace. The proposed MSCR design is enabled by integrating a theoretical model and the genetic algorithm. The current work not only achieves the optimal workspace for MSCRs but also provides a powerful tool for the efficient design and optimization of future magnetic soft robots and actuators.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
•A new damage indicator, Modified Cornwell Indicator (MCI).•MCI performs more efficient then Cornwell Indicator (CI).•MCI is combined with Genetic Algorithm (GA), MCI-GA.•MCI-GA provides more ...accurate and efficient results than other techniques in the literature.
This paper presents a new methodology for damage identification and quantification in two- and three-dimensional structures. The application of the proposed methodology is investigated numerically using Finite Element Method (FEM) and Matlab program. We propose a Modified Cornwell Indicator (MCI) that performs more efficient in damage detection than the standard Cornwell Indicator (CI). Furthermore, MCI is combined with Genetic Algorithm (GA) for further quantification of the detected damage. In GA, MCI, is used as an objective function to compare between measured and calculated indicators. The results of the analysis show that the proposed technique is accurate and efficient, when compared with other techniques in the literature, to estimate the severity of structural damage.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial ...diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based CVD diagnosis and treatment. Our study presents a deep neural network (DNN) strategy to ameliorate the aforementioned difficulties. Our strategy consists of a learning stage where classification accuracy is improved via a robust feature extraction protocol. This is followed by using a genetic algorithm (GA) process to aggregate the best combination of feature extraction and classification. Comparison of the performance recorded for the proposed technique alongside state-of-the-art methods reported the area shows an increase of 0.94 and 0.953 in terms of average accuracy and F1 score, respectively. The outcomes suggest that the proposed model could serve as an analytic module to alert users and/or medical experts when anomalies are detected.
•Dynamic reconfiguration with GA for TCT PV array to disperse the shading effect.•The new technique obtains the optimal configuration and improve the generated power.•The proposed technique is ...applied for different sizes of PV array.•The proposed technique overcomes the issue of scaling to larger applications.
Photovoltaic (PV) plants can be exposed to partial shading, which reduces the energy production and causes multi-peaks to form in the Power-Voltage (P-V) curve. As a result, the row currents of the PV modules will not be constant. Several techniques have been proposed to overcome partial shading, such as the static and dynamic reconfiguration techniques, with both aiming to reduce the difference in the row currents to improve energy production. Minimization of the row current via static techniques requires laborious work and extra wiring. On the other hand, dynamic techniques require an extensive monitoring system to support different tasks. Therefore, to improve the power generated from the PV array, this paper suggests a new reconfiguration technique for PV panels using Genetic algorithm (GA) and two main reconfigurable steps based on a switching matrix. In this technique, only the electrical connections of the PV panels are changed while its physical location remains unchanged. To verify the effectiveness of the proposed reconfiguration technique, the system was simulated and tested using MATLAB/SIMULINK software, with four shading patterns. The results were compared with other reconfiguration techniques, namely TCT configuration, competence square (CS), SuDoKu, two-phase array reconfiguration, Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Modified Harris Hawks Optimization (MHHO). The performance of each shading case was also analyzed. Also, a comparative study on performance analysis in real-time application was carried out for each shading pattern. The results prove the superiority of the proposed technique over other techniques for overcoming partial shading.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZRSKP
•The wellhead back pressure control model is established and transfer function is calculated.•An improved genetic algorithm optimization fuzzy PID controller is designed.•Two experiments are designed ...to validate the great advantages of the developed model.
The throttle valve is the core equipment to managed pressure drilling (MPD) technology. PID controller is the most widely used throttle valve control algorithm. However, in the wellhead back pressure control system, the control of the throttle valve has strong nonlinearity and time variability. This makes precise closed-loop control of wellhead back pressure a challenge. The traditional controller needs to be improved in terms of control speed, stability and robustness. To overcome these shortcomings, this paper proposes an improved genetic algorithm optimization fuzzy controller. Firstly the wellhead back pressure control model is established and transfer function is calculated. Secondly, an improved genetic algorithm to optimize the highly nonlinear fuzzy control rules between the input and response in the fuzzy PID controller is designed. Finally, four traditional controllers are compared with the developed model to prove the method is optimal. The proposed controller has excellent performance in terms of time response parameters (such as rise time, adjustment time, overshoot, and steady-state error). The controller exhibits great advantages in terms of speed, stability, and robustness, which significantly improves the performance of the wellhead back pressure control system.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A novel algorithm was introduced for path planning in continuous environment.•A deterministic algorithm was introduced to determine all feasible initial paths.•A Genetic Algorithm with five new ...crossover and mutation operators was developed.•Path length, smoothness, and safety were combined to form the objective function.•The proposed algorithm was extended to handle the multi-robot problem.
This paper presents a hybrid approach for path planning of multiple mobile robots in continuous environments. For this purpose, first, an innovative Artificial Potential Field (APF) algorithm is presented to find all feasible paths between the start and destination locations in a discrete gridded environment. Next, an enhanced Genetic Algorithm (EGA) is developed to improve the initial paths in continuous space and find the optimal path between start and destination locations. The proposed APF works based on a time-efficient deterministic scheme to find a set of feasible initial paths and is guaranteed to find a feasible path if one exists. The EGA utilizes five customized crossover and mutation operators to improve the initial paths. In this paper, path length, smoothness, and safety are combined to form a multi-objective path planning problem. In addition, the proposed method is extended to deal with multiple mobile robot path planning problem. For this purpose, a new term is added to the objective function which measures the distance between robots and a collision removal operator is added to the EGA to remove possible collision between paths. To assess the efficiency of the proposed algorithm, 12 planar environments with different sizes and complexities were examined. Evaluations showed that the control parameters of the proposed algorithm do not affect the performance of the EGA considerably. Moreover, a comparative study has been made between the proposed algorithm, A*, PRM, B-RRT and Particle Swarm Optimization (PSO). The comparative study showed that the proposed algorithm outperforms PSO as well as well-recognized deterministic (A*) and probabilistic (PRM and B-RRT) path planning algorithms in terms of path length, run time, and success rate. Finally, simulations proved the efficiency of the proposed algorithm for a four-robot path planning problem. In this case, not only the proposed algorithm determined collision-free paths, but also it found near optimal solution for all robots.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The paper presents an algorithm for integrated scheduling, dispatching, and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm. The algorithm generates an ...integrated schedule and detail routing paths while optimizing makespan, AGV travel time, and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The multi-objective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance improvements of the last two previous generations. Computational experiments was conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. Integrated scheduling of jobs in FMS which are in synchrony with AGV dispatching, scheduling, and routing proved to ensure the feasibility and effectiveness of all the solutions of the integrated constituent elements.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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