Hybrid Evolutionary Technique Research Articles

Implementation of AI for forecasting of wind speed and solar irradiation using different Machine Learning techniques

High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and wind systems, they depend on the weather, as compared to the conventional generation. As we know, the balance in managing load and generated power in energy system is very import ant. If the power which is supplied from solar and wind perfectly predictable, the extra cost of operating power system with a large penetration of renewable energy will be reduced. Since, the accurate and reliable forecasting system for renewable sources represents an important topic as a major contribution for increasing non-programmable renewable on over the world. The target of this research is to describe the advanced hybrid evolutionary techniques of computational intelligence applied for PV as well as wind power forecast. The evaluation of this investigation is obtained by comparing different definitions of the forecasting error. Moreover, the meaning of NWP (numerical weather prediction) values based on meteorological information on solar and wind power forecasting at Indore has been highlighted in this research.

OCC-MP: An optimal cluster based congestion aware technique multipath routing protocol in WSN using hybrid evolutionary techniques

Due to massive data traffic, wireless network congestion is inevitable, causing network performance deterioration and data transmission quality reduction. By adjustingthe traffic flow at the source node along the optimal path, Wireless sensor networks strive to evade and overcome congestion. Recently, numerous evolutionary approaches for congestion detection and avoidance have been presented, although they do not improve WSN performance. A hybrid evolutionary approach is attempted to avoid congestion in WSN and improve network performance. We present an optimum cluster-based congestion aware multipath routing protocol (OCC-MP). An improved atom search optimization (IASO) approach for efficient clustering is introduced initially in OCC-MP. Then the HSIPO method is used for decision making, which computes each node's trust degree. The HSIPO algorithm selects the cluster head (CH) from a group of nodes. Then a deep recurrent neural network (DRNN) is used to monitor congestion and provide congestion aware routing. Finally, multiple simulation situations like various node densities andat various simulation rounds supported our proposed OCC-MP techniqueoutperformedcurrent methodologies with respect to energy consumption, throughput, traffic load overflow, delivery ratio, and number of nodes alive.

Output-only structural damage identification based on Q-learning hybrid evolutionary algorithm and response reconstruction technique

Various structural damage identification methods have been developed and employed, while the absence of input excitation measurements may pose a huge challenge in their application since the input forces such as seismic load, traffic load, wind load, are not directly measurable. To address this issue, in the present paper, a novel output-only structural damage detection approach based on Q-learning hybrid evolutionary algorithm (QHEA) and response reconstruction technique is presented. On the one hand, the external excitation and structural acceleration responses are reconstructed with the aid of response reconstruction and Tikhonov regularization techniques in time domain. Structural damage identification can be formulated as an optimization-based inverse problem. On the other hand, a new optimization framework QHEA integrating Jaya algorithm, differential evolution, Q-learning algorithm is developed as search tool. The unknown structural parameters and unmeasured input force are iteratively updated by using two different measurement sets until the reconstructed acceleration responses agree well with the measured responses. Numerical examples involving a cantilever beam structure under single excitation and a simply-supported 51-bar truss structure under multiple excitations, as well as an experimental five-floor steel frame structure in the laboratory are carried out to validate the effectiveness of the proposed method. The final results demonstrate that the proposed method can accurately detect damage locations and quantify damage extents without the information of input excitation. In addition, the superiority of QHEA over other heuristic algorithms, the uncertainties of measurement noise and modeling errors on damage identification results are further examined.

Psoriasis Lesion Detection Using Hybrid Seeker Optimization-based Image Clustering.

In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a prerequisite for quantifying the severity of this disease. However, segmentation of psoriatic lesions cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence. An alternative method for psoriatic lesion segmentation with objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique that possesses a higher probability of global convergence for psoriasis lesion segmentation. A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms, namely Artificial Bee Colony and Seeker Optimization algorithm, has been proposed. The initial population for the hybrid technique is obtained from the two conventional local- based approaches, i.e., Fuzzy C-means and K-means clustering algorithms. The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with a Jaccard Index of 0.91 from 720 psoriasis images. The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.

Improving efficacy of Cr (VI) adsorption process on sustainable adsorbent derived from waste biomass (sugarcane bagasse) with help of ant colony optimization

A hexavalent form of chromium, which is a major pollutant found in water bodies, has lethal impacts on aquatic life and human beings. Among various effluent treatment techniques, the adsorption process was considered as an effective technique, but the cost of adsorbents hinders the viability of this process. In this research study, sugarcane bagasse based agricultural biomass waste in the form of activated granular carbon is used as an adsorbent to study its sustainability for chromium removal from effluent water in a batch process. The efficacy of the process was evaluated by validating various equilibrium isotherm and kinetic models. As the contact temperature is increased, resulted in a higher percentage of chromium removal for all three different initial concentrations. Adsorbent prepared at higher activation resulted in lower chromium removal. A novel inverse modeling technique integrated with hybrid evolutionary ant colony optimization technique was employed to estimate the isotherm/kinetic parameters. The systematic analysis revealed the mechanisms, inherent characteristics in the adsorption process, which forms the base in the design of industrial adsorption process and thus optimize the process parameters.

Editorial for Special Issue on “Hybrid Evolutionary and Swarm Techniques for Big Data Analytics and Applications”

Editorial for Special Issue on “Hybrid Evolutionary and Swarm Techniques for Big Data Analytics and Applications”

Integrated process planning, WSPT scheduling and WSLK due-date assignment using genetic algorithms and evolutionary strategies

Process planning, scheduling and due-date assignment are three important manufacturing functions in our life. They all try to get local optima and there can be enormous loss in overall performance value if they are handled separately. That is why they should be handled concurrently. Although integrated process planning and scheduling with due date assignment problem is not addressed much in the literature, there are numerous works on integrated process planning and scheduling and many works on scheduling with due date assignment. Most of the works in the literature assign common due date for the jobs waiting and due dates are determined without taking into account of the weights of the customers. Here process planning function is integrated with weighted shortest processing times (WSPT) scheduling and weighted slack (WSLK) due date assignment. In this study unique due dates are given to each customer and important customers gets closer due dates. Integration of these three functions is tested for different levels of integration with genetic algorithms, evolutionary strategies, hybrid genetic algorithms, hybrid evolutionary strategies and random search techniques. Best combinations are found as full integration with genetic search and hybrid genetic search. Integration of these three functions provided substantial improvements in global performance.

Multi-Objective Optimization in a Job Shop with Energy Costs through Hybrid Evolutionary Techniques

Energy costs are an increasingly important issue in real-world scheduling, for both economic and environmental reasons. This paper deals with a variant of the well-known job shop scheduling problem, where we consider a bi-objective optimization of both the weighted tardiness and the energy costs. To this end, we design a hybrid metaheuristic that combines a genetic algorithm with a novel local search method and a linear programming approach. We also propose an efficient procedure for improving the energy cost of a given schedule. In the experimental study we analyse our proposal and compare it with the state of the art and also with a constraint programming approach, obtaining competitive results.

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.

Designing foam filled sandwich panels for blast mitigation using a hybrid evolutionary optimization algorithm

Developing sandwich structures with high energy absorption capability is important for shock loading applications. In the present study, a hybrid evolutionary optimization technique based on Multi-Island Genetic Algorithm and Hooke-Jeeves Algorithm is used in the design stage of the sandwich structures to obtain effective results. Optimum parameters of cell geometry were investigated using the hybrid optimization algorithm to design foam filled sandwich panels for three main boundary conditions. Shock tube experiments were conducted in order to simulate the shock load effects along with 3D and 2D finite element analysis. Using the experimental results, a simulation-based design optimization approach was prepared and used to develop the designs of new sandwich structures. Promising results were obtained for all three different boundary conditions. In the simply supported case, 21% improvement of shock absorption was achieved by using 57% less volume of foam with respect to the original fully foam filled sandwich panel. In the clamped-clamped case, 16% improvement of shock absorption with 52% less volume was obtained. In the rigid base case study, 6% improvement of shock absorption with 38% less volume usage was achieved. The structures developed in this study will be of use in the defense, automotive and other industries.

Quantum seeded evolutionary computational technique for constrained optimization in engineering design and manufacturing

In this paper an attempt is made to develop a new Quantum Seeded Hybrid Evolutionary Computational Technique (QSHECT) that is general, flexible and efficient in solving single objective constrained optimization problems. It generates initial parents using quantum seeds. It is here that QSHECT incorporates ideas from the principles of quantum computation and integrates them in the current framework of Real Coded Evolutionary Algorithm (RCEA). It also incorporates Simulated Annealing (SA) in the selection process of Evolutionary Algorithm (EA) for child generation. The proposed algorithm has been tested on standard test problems and engineering design problems taken from the literature. In order to test this algorithm on domain-specific manufacturing problems, Neuro-Fuzzy (NF) modeling of hot extrusion is attempted and the NF model is incorporated as a fitness evaluator inside the QSHECT to form a new variant of this technique, i.e. Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Computational Technique (QSNFHECT) and is effectively applied for process optimization of hot extrusion process. The neuro-fuzzy model (NF) is also compared with statistical regression analysis (RA) model for evaluating the extrusion load. The NF model was found to be much superior. The optimal process parameters obtained by Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Technique (QSNFHECT) are validated by the finite element model. The proposed methodology using QSNFHECT is a step towards meeting the challenges posed in intelligent manufacturing systems and opens new avenues for parameter estimation and optimization and can be easily incorporated in existing manufacturing setup.

Computational model for predicting the effect of process parameters on surface characteristics of mass finished components

Purpose Mass finishing is a commonly employed surface finishing process for improving surface characteristics of aerospace engineering components. Optimization of surface characteristics of such critical components require an explicit computational model that can describe the surface characteristics of the finished component. This paper aims to develop an explicit computational model that can describe the surface roughness as a function of various process parameters which influence the mass finishing process. Design/methodology/approach In the present work, the authors propose to study the roughness characteristics using a combined evolutionary computing approach based on Multi-Adaptive Regression Splines (MARS) and Genetic Programming (GP) techniques. Findings The authors conducted sensitivity and parametric analysis to capture the dynamics of surface characteristics by unveiling dominant input variables and hidden non-linear relationships. It is found that by regulating the process time and media size, a greatest variation in surface finish reduction can be achieved in mass finishing process. Originality/value To the best of authors knowledge, for the first time a hybrid evolutionary computational technique has been proposed in this work. The authors combined two powerful evolutionary techniques, namely Multi-variate Adaptive Regressive Splines and Genetic Programming approach. The proposed approach was able to capture the dynamics of surface roughness with higher accuracy as comparable to that of the experiments.

An alternative hybrid evolutionary technique focused on allocating machines and sequencing operations

Fil: Frutos, Mariano. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Bahia Blanca. Instituto de Investigaciones Economicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economia. Instituto de Investigaciones Economicas y Sociales del Sur; Argentina. Universidad Nacional del Sur; Argentina

An Approach to Urban Quarter Design Using Building Generative Design and Thermal Performance Optimization

Buildings thermal performance is influenced by the urban context, such as adjacent buildings shadows, block wind paths, or solar radiation reflection. For this reason, in this paper, an automated procedure is used to generate and optimize buildings’ thermal performance in a closed O-shape urban quarter with ten building blocks to determine the importance of including the surroundings in estimating the buildings’ thermal behavior. The overall shape of the urban quarter is pre-designed, being mainly residential, with several stores in the ground floor and limited to three levels. Each building will have four alternative designs created using a hybrid evolutionary strategy technique that generates building's floor plans according to practitioner's preferences and requirements. Then, a sequential variable optimization procedure coupled with dynamic simulation engine is used to explore the improvement potential of each solution by changing and adding several building elements. The final quarter design is determined by combining the best of all buildings’ solutions from thermal performance criteria.The results demonstrate the influence of urban context in the resulting building's thermal performance. Despite the building's shape is similar in the four solutions, these have significant thermal behavior difference due to their interior organization and position in the urban quarter. A comparison analysis is carried out between all building block designs. The buildings, which have exterior walls with openings facing south, have almost half degree-hours of thermal discomfort due to the combination of large openings and shading overhangs. It is also possible to conclude that generative tools, enhanced with optimization procedures, may help practitioners in designing more energy efficient buildings.

5D Parameter Estimation of Near-Field Sources Using Hybrid Evolutionary Computational Techniques

Hybrid evolutionary computational technique is developed to jointly estimate the amplitude, frequency, range, and 2D direction of arrival (elevation and azimuth angles) of near-field sources impinging on centrosymmetric cross array. Specifically, genetic algorithm is used as a global optimizer, whereas pattern search and interior point algorithms are employed as rapid local search optimizers. For this, a new multiobjective fitness function is constructed, which is the combination of mean square error and correlation between the normalized desired and estimated vectors. The performance of the proposed hybrid scheme is compared not only with the individual responses of genetic algorithm, interior point algorithm, and pattern search, but also with the existing traditional techniques. The proposed schemes produced fairly good results in terms of estimation accuracy, convergence rate, and robustness against noise. A large number of Monte-Carlo simulations are carried out to test out the validity and reliability of each scheme.

Hybrid evolutionary techniques in feed forward neural network with distributed error for classification of handwritten Hindi ‘SWARS’

In this work, the performance of feedforward neural network with a descent gradient of distributed error and the genetic algorithm (GA) is evaluated for the recognition of handwritten ‘SWARS’ of Hindi curve script. The performance index for the feedforward multilayer neural networks is considered here with distributed instantaneous unknown error i.e. different error for different layers. The objective of the GA is to make the search process more efficient to determine the optimal weight vectors from the population. The GA is applied with the distributed error. The fitness function of the GA is considered as the mean of square distributed error that is different for each layer. Hence the convergence is obtained only when the minimum of different errors is determined. It has been analysed that the proposed method of a descent gradient of distributed error with the GA known as hybrid distributed evolutionary technique for the multilayer feed forward neural performs better in terms of accuracy, epochs and the number of optimal solutions for the given training and test pattern sets of the pattern recognition problem.

An approach to the multi-level space allocation problem in architecture using a hybrid evolutionary technique

Abstract A multi-level allocation problem consists in determining the position and size of spaces on several levels according to the user's requirements and constraints. How levels are connected and how vertical circulation objects are treated may limit the practitioner's preferences or reduce the number of solutions. Literature review has shown that in the multi-level allocation problem very often stairs and elevators are considered as fixed and rigid objects during the search process and sometimes even neglected. This paper presents an approach to deal with multi-levels based on a hybrid evolutionary technique where stairs and elevators are parametric objects, which interact with other spaces during the search process. Three case studies are presented, where the algorithm is tested on handling large and complex problems, how concurrent vertical circulation objects interact, and how several levels are stacked within a building boundary. The algorithm shows to be capable of generating coherent multi-level floor plans.

Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error.

An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, Part 2: Validation and performance tests

The first part of this paper proposed a hybrid evolutionary technique which helps architects to generate sets of floor plans in the early design stage. The algorithm is an enhanced Evolutionary Strategy (ES) with a Stochastic Hill Climbing (SHC) technique.In this second part, the validity and performance of the technique is examined. Four tests are conducted. Three validation tests are used to determine the ability of the algorithm to replicate a floor plan made by an architect; the ability to produce a set of different floor plan designs by comparing the results with a well-known enumerated floor plan solution; its capability to work with less constrained problems, and to control the variation in the floor plan form with different compactness evaluators’ weights. The last test determines the evolving behavior, the breakdown of the evaluators’ performance for the objective function, and the robustness of the algorithm.The methodologies used in the tests and the problem specifications are presented. Results and observations on the evaluators’ weights and their importance and the limitations of the technique are analyzed and discussed. Finally, the conclusion to this part of the paper is made.

Hybrid evolutionary optimal MEMS design

A hybrid evolutionary design synthesis and optimization process for microelectromechanical systems (MEMS) devices has been developed. The process integrates a MEMS design component library with multiple simulation modules and two levels of design optimization: global multi-objective genetic algorithms (MOGA) and local gradient-based refinement. During the hybrid evolutionary design process, MOGA randomly searches the design space and approaches the desirable design solutions using probabilistic transition rules, and gradient-based local optimization refines promising design candidates with computational efficiency. To efficiently apply hybrid evolutionary optimization techniques on MEMS designs, a hierarchical tree-structured component-based genotype representation has been developed, which incorporates specific engineering knowledge into the design synthesis and optimization process. The MEMS design component library serves as a source of practical and efficient genotypes for the evolutionary process, with each component associated with its instructions and restrictions on genetic operations. The component-based genotype incorporated with engineering knowledge constrains evolutionary searching in appropriate and promising regions of the search space, allowing a deeper search in a given amount of time. Hybrid evolutionary MEMS design synthesis and optimization are demonstrated with surface-micromachined resonator and accelerometer designs.