A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Integrated Robust Design Using Response Surface Methodology and Constrained Optimization

System design, parameter design, and tolerance design are the three stages of product or process development advocated by Genichi Taguchi. Parameter design, or robust parameter design (RPD), is the method to determine nominal parameter values of controllable variables such that the quality characteristics can meet the specifications and the variability transmitted from uncontrollable or noise variables is minimized for the process or product. Tolerance design is used to determine the best limits for the parameters to meet the variation and economical requirements of the design. In this thesis, response surface methodology (RSM) and nonlinear programming methods are adopted to integrate the parameter and tolerance design. The joint optimization method that conducts parameter design and tolerance design simultaneously is more effective than the traditional sequential process. While Taguchi proposed the crossed array design, the combined array design approach is more flexible and efficient since it combines controllable factors, internal noise factors, and external noise factors in a single array design. A combined array design and the dual response surface method can provide detailed information of the process through process mean and process variance obtained from the response model. Among a variety of cuboidal designs and spherical designs, standard or modified central composite designs (CCD) or face-centered cube (FCC) designs are ideal for fitting second-order response surface models, which are widely applied in manufacturing processes. Box-Behnken design (BBD), mixed resolution design (MRD), and small composite design (SCD) are also discussed as alternatives. After modeling the system, nonlinear programming can be used to solve the constrained optimization problem. Dual RSM, mean square error (MSE) loss criterion, generalized linear model, and desirability function approach can be selected to work with quality loss function and production cost function to formulate the object function for optimization. This research also extends robust design and RSM from single response to the study of multiple responses. It was shown that the RSM is superior to Taguchi approach and is a natural fit for robust design problems. Based on our study, we can conclude that dual RSM can work very well with ordinary least squares method or generalized linear model (GLM) to solve robust parameter design problems. In addition, desirability function approach is a good selection for multiple-response parameter design problems. It was confirmed that considering the internal noise factors (standard deviations of the control factors) will improve the regression model and have a more appropriate optimal solution. In addition, simulating the internal noise factors as control variables in the combined array design is an attractive alternative to the traditional method that models the internal noise factors as part of the noise variables. The purpose of this research is to develop the framework for robust design and the strategies for RSM. The practical objective is to obtain the optimal parameters and tolerances of the design variables in a system with single or multiple quality characteristics, and thereby achieve the goal of improving the quality of products and processes in a cost effective manner. It was demonstrated that the proposed methodology is appropriate for solving complex design problems in industry applications

Design of small-scale hybrid energy systems taking into account generation and demand uncertainties

The adoption of energy systems powered by renewable sources requires substantial economic investments. Hence, selecting system components of an appropriate size becomes a critical step, which is significantly influenced by their distinct characteristics. Furthermore, the availability of renewable energy varies over time, and estimating this availability introduces considerable uncertainty. In this paper, we present a technique for the optimal design of hybrid energy systems that accounts for the uncertainty associated with resource estimation. Our method is based on stochastic programming theory and employs a surrogate model to estimate battery lifespan using a feedforward neural network (FFNN). The optimization analysis for system design was conducted using a genetic algorithm (GA) and the poplar optimization algorithm (POA). We assessed the effectiveness of the proposed technique through a hypothetical case study. The introduction of a surrogate model, based on an FFNN, resulted in an approximation error of 9.6 % for cost estimation and 20.6 % for battery lifespan estimation. The probabilistic design indicates an energy system cost that is 25.7 % higher than that obtained using a deterministic approach. Both the GA and POA achieved solutions that likely represent the global optimum

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Multidisciplinary aerospace design optimization: Survey of recent developments

The increasing complexity of engineering systems has sparked increasing interest in multidisciplinary optimization (MDO). This paper presents a survey of recent publications in the field of aerospace where interest in MDO has been particularly intense. The two main challenges of MDO are computational expense and organizational complexity. Accordingly the survey is focussed on various ways different researchers use to deal with these challenges. The survey is organized by a breakdown of MDO into its conceptual components. Accordingly, the survey includes sections on Mathematical Modeling, Design-oriented Analysis, Approximation Concepts, Optimization Procedures, System Sensitivity, and Human Interface. With the authors' main expertise being in the structures area, the bulk of the references focus on the interaction of the structures discipline with other disciplines. In particular, two sections at the end focus on two such interactions that have recently been pursued with a particular vigor: Simultaneous Optimization of Structures and Aerodynamics, and Simultaneous Optimization of Structures Combined With Active Control

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)

Study on multi-objective optimization of circuit design by evolutionary computation technologies

制度:新 ; 報告番号:甲3364号 ; 学位の種類:博士(工学) ; 授与年月日:2011/4/25 ; 早大学位記番号:新568

AI-based hybrid optimisation of multi-megawatt scale permanent magnet synchronous generators for offshore wind energy capture

The finite nature of earth’s natural resources has become a post-industrial reality. Despite their alarming depletion, fossil fuels still dominated the global final energy landscape. Technological advances and rapid deployment of various renewable energy technologies have demonstrated their potential at reducing the worlds dependency on fossil fuels and their negative impacts. Presently, wind energy is the most cost-effective means of renewable energy conversion in the developed world and has currently has a price point that is in direct competition with fossil fuel. Coupled with the low price, the adoption of wind power has seen capacity increases in excess of 650% over the last ten years. Permanent Magnet Synchronous Generators (PMSGs) have become prominent in large wind energy system applications. The Radial Flux machine topology has become particularly attractive. In order to improve the competitiveness of large wind energy systems, the main focal point of current research is toward reducing the Levelised Cost of Energy (LCOE) of the systems. A proven method of reducing the LCOE of wind power generation is by upscaling RF-PMSGs to the multi mega-watt (MW) range. For the much wider adoption of wind power generation, the cost of energy (price/MWh) needs to be driven down further, by the development of more efficient and cost-effective ways to harvest the vast amounts of energy. The main objective of this dissertation is the drive-train selection, detailed design, sizing and optimisation of a 10.8 MW permanent magnet radial flux synchronous generator (RF-PMSG) to be used in the next generation of offshore wind farms. From an analytical viewpoint, the results suggested the use of a medium speed RF-PMSG utilizing a single-stage geared drivetrain, together with a MV voltage rating (3.3kV) for the 10.8 MW RF-PMSG designed in the thesis. Finally, this dissertation proposes a promising hybrid, analytical-numerical optimisation of a 10.8 MW RF-PMSG to be used for offshore Wind Energy Conversion. The hybrid optimisation utilises a two-stage optimisation strategy that incorporates both an analytical and a numerical (FEA) optimisation; using the DE algorithm and the Taguchi method respectively. Although the permanent magnet losses are neglected in the analytical loss calculations, they are included in the numerical FE portion of the hybrid optimisation. The initial stage (STAGE I) of the hybrid optimisation utilised the DE algorithm. The objective function was set to reduce the initial cost (!"#""*+) in the generator, i.e. NdFeB PM mass (',-), copper mass (').), and active steel in the stator lamination and rotor core ('/0++&), while maintaining a pmsg efficiency (23456 ≥ 97%). The initial stage saw a reduction in initial cost by 25.5%, while maintaining an efficiency of 23456 = 97.8%. The final stage (STAGE II) of the hybrid optimisation utilising the Taguchi method, to make improvements on the performance of the machine, by optimising the Torque and back EMF characteristics while further reducing the NdFeB PM mass. The Magnet Fill Factor (APM), the Slot opening (bs0), the thickness of the permanent magnet poles (ℎ34) and the equivalent length of the air gap (?6) were used as optimisation variables. The final stage saw a decrease in cogging torque (@)06) by 53.4%, an increase in average torque (@%*) by 1.1%, a reduction in the total harmonic distortion of the back EMF (@AB) by 8.0%, a reduction in the required mass of the NdFeB permanent magnet material by 12.43%, while maintaining a torque ripple (@C"3) < 10%. The RF-PMSG characteristics optimised using the hybrid analytical-numerical optimisation were hypothesised to contribute in a reduction of the LCOE of offshore wind energy both in terms of Operational expenditure (OPEX) and Capital expenditure (CAPEX)

TEACHING-LEARNING-BASED PARAMETRIC OPTIMIZATION OF AN ELECTRICAL DISCHARGE MACHINING PROCESS

Due to several unique features, electrical discharge machining (EDM) has proved itself as one of the efficient non-traditional machining processes for generating intricate shape geometries on various advanced engineering materials in order to fulfill the requirement of the present day manufacturing industries. In this paper, the machining capability of an EDM process is studied during standard hole making operation on pearlitic SG iron 450/12 grade material, while considering gap voltage, peak current, cycle time and tool rotation as input parameters. On the other hand, material removal rate, surface roughness, tool wear rate, overcut and circularity error are treated as responses. Based on single- and multi-objective optimization models, this process is optimized using the teaching-learning-based optimization (TLBO) algorithm, and its performance is contrasted against firefly algorithm, differential evolution algorithm and cuckoo search algorithm. It is revealed that the TLBO algorithm supersedes the others with respect to accuracy and consistency of the derived optimal solutions, and computational efforts