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Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5 % and 2.3 % with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32 %, this result represents an annual cost savings of 42 % on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method. [Display omitted] • Hybrid supercapacitor-integrated dispatch model with analysis of pattern search method. • Dispatch-coupled sizing renewable sources and energy storage with power smoothing, improving energy quality. • Numerical analysis on different size optimization methods comparatively considering experimental data. • An improved pattern search optimization that exceeds the standard one. • Validation of the effectiveness of the proposed framework in a laboratory microgrid test system. [ABSTRACT FROM AUTHOR]

Adaptive dimensional search (ADS) algorithm is a recently proposed metaheuristic optimization technique for discrete structural optimization problems. In this study, discrete sizing optimization problem of steel frames is tackled using the ADS algorithm. An important feature of the algorithm is that it does not use any metaphor as an underlying principle for its implementation. Instead, the algorithm employs an efficient performance-oriented methodology at each iteration for convergence to the optimum or a near optimum solution. The performance of the ADS is investigated through optimum design of five real-size steel frame structures and the results are compared versus several contemporary metaheuristic techniques. The comparison of the obtained numerical results with those of available designs in the literature reveals the reliability and efficiency of the ADS in optimum design of steel frames. [ABSTRACT FROM AUTHOR]

This paper focuses on discrete sizing optimization of frame structures using commercial profile catalogs. The optimization problem is formulated as a mixed-integer linear programming (MILP) problem by including the equations of structural analysis as constraints. The internal forces of the members are taken as continuous state variables. Binary variables are used for choosing the member profiles from a catalog. Both the displacement and stress constraints are formulated such that for each member limit values can be imposed at predefined locations along the member. A valuable feature of the formulation, lacking in most contemporary approaches, is that global optimality of the solution is guaranteed by solving the MILP using branch-and-bound techniques. The method is applied to three design problems: a portal frame, a two-story frame with three load cases and a multiple-bay multiple-story frame. Performance profiles are determined to compare the MILP reformulation method with a genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a genetic programming approach for simultaneous optimization of sizing and topology of truss structures. It aims to find the optimal cross-sectional areas and connectivities of the joints to achieve minimum weight in the search space. The structural optimization problem is subjected to kinematic stability, maximum allowable stress and deflection. This approach uses the variable-length representation of potential solutions in the shape of computer programs and evolves to the optimum solution. This method has the capability to identify redundant truss elements and joints in the design space. The obtained results are compared with existing popular and competent techniques in literature and its competence as a tool in the optimization problem are demonstrated in solving some benchmark examples, the proposed approach provided lighter truss structures than the available solutions reported in the literature. [ABSTRACT FROM AUTHOR]

Heat transfer search (HTS) is a novel metaheuristic optimization algorithm that simulates the laws of thermodynamics and heat transfer. In this study, the HTS algorithm is adapted to truss structure optimization. Sizing optimization searches for the minimum weight of a structure subject to stress and displacement constraints. Three truss structures often taken as benchmarks in the optimization literature are selected here in order to verify the efficiency and robustness of the HTS algorithm. Optimization results indicate that HTS can obtain better designs (i.e. lighter trusses) than most of the state-of-the-art metaheuristic optimizers. The convergence behaviour of HTS also is as good as the other algorithms. [ABSTRACT FROM AUTHOR]

This article proposes a method called the cooperative coevolutionary genetic algorithm with independent ground structures (CCGA-IGS) for the simultaneous topology and sizing optimization of discrete structures. An IGS strategy is proposed to enhance the flexibility of the optimization by offering two separate design spaces and to improve the efficiency of the algorithm by reducing the search space. The CCGA is introduced to divide a complex problem into two smaller subspaces: the topological and sizing variables are assigned into two subpopulations which evolve in isolation but collaborate in fitness evaluations. Five different methods were implemented on 2D and 3D numeric examples to test the performance of the algorithms. The results demonstrate that the performance of the algorithms is improved in terms of accuracy and convergence speed with the IGS strategy, and the CCGA converges faster than the traditional GA without loss of accuracy. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the cycle-based sizing optimization for diesel-engine two-cell selective catalytic reduction (SCR) systems. In modeling the SCR system, it is generally assumed that the states inside the catalyst can are homogeneous to simplify the modeling work. However, the actual states are not homogeneous, and the modeling error becomes large if the catalyst length is long. To reduce the modeling error, the states are assumed piecewise homogeneous, i.e., the catalyst is sliced into multicells and the states for each cell are assumed homogeneous. In this paper, we focus on the two-cell SCR systems where the two cells are connected in series and where the chemical reaction rates are assumed identical for the two cells. Considering the main chemical reactions and assuming that each cell is a continuous stirred tank reactor, a nonlinear system model with six states is obtained. To simultaneously reduce the tailpipe \mboxNOx and ammonia slip, it is quite interesting to study the optimal control of the urea dosing such that the \mboxNOx emissions can be maximally converted and where the ammonia slip is constrained. A dynamic programming (DP) algorithm that has a tolerable computational load is proposed. Since the whole catalyst is sliced into two cells, the next motivation is how to divide the catalyst such that the \mboxNOx conversion efficiency can be maximized while meeting the tailpipe ammonia constraint. The best sizing ratio of the SCR system in our application for the US06 test cycle is found to be 60%–40%. [ABSTRACT FROM PUBLISHER]

The recently developed flower pollination algorithm is used to minimize the weight of truss structures, including sizing design variables. The new algorithm can efficiently combine local and global searches, inspired by cross-pollination and self-pollination of flowering plants, respectively. Furthermore, it implements an iterative constraint handling strategy where trial designs are accepted or rejected based on the allowed amount of constraint violation that is progressively reduced as the search process approaches the optimum. This strategy aims to obtain always feasible optimized designs. The new algorithm is tested using three classical sizing optimization problems of 2D and 3D truss structures. Optimization results show that the proposed method is competitive with other state-of-the-art metaheuristic algorithms presented in the literature. [ABSTRACT FROM AUTHOR]

This paper presents the applications of the mine blast algorithm (MBA) and the water cycle algorithm (WCA), in addition to an improved version of MBA for weight minimization of truss structures including discrete sizing variables. The MBA mimics the explosion of landmines, while the WCA is inspired by the observation of water cycle process. An improved version of MBA (IMBA), is also presented. The efficiency of the three optimization algorithms is tested using classical benchmark discrete truss design problems. Optimization results show that MBA, IMBA, and WCA offer a good degree of competitiveness against other state-of-the-art metaheuristic techniques. [ABSTRACT FROM AUTHOR]

• Sensitivity of the optimal design found on a large-scale steel frame to the size of the discrete design domain is investigated. • The size of the discrete design space is adjusted through member grouping modifications and limiting the available set of discrete cross-sections. • A recently develop metaheuristic optimization algorithm is employed to explore and (subsequently) exploit the large solution space of candidate designs. • Several combinations of member groups and standard steel profile lists were numerically tested on a large-scale (real-world) 3D steel frame. • A reduction of approximately 33% in the optimal weight was observed between member group and steel profile list combinations generating the smallest and largest design spaces, respectively. This paper examines the effect of member grouping and selected pool size of discrete cross-sections on the optimal design of a large-scale steel frame structure. Member grouping is commonly applied to structural optimization problems to significantly reduce the solution space of candidate designs and improve the efficiency and robustness of the search to achieve near-optimum solutions. At the same time, discrete sets of cross-sectional steel shapes (profile lists) assigned to a particular member group are often limited to a subset of cross-sections to further limit the search space and ensure practicality in the final design. However, for large-scale structural systems (e.g., tall buildings), limiting the number of member groups coupled with the exclusion of lighter cross-sectional shapes may significantly impact the quality of the optimal solution (i.e., weight of the frame). This work presents a case study of a large-scale three-dimensional (3D) steel frame to evaluate the sensitivity of the optimal design to practical combinations of member groups and steel profile lists used in multi-story steel frames. A recently developed metaheuristic search algorithm was applied to the steel frame and independent optimization experiments were performed for 16 unique member grouping and profile list combinations (cases) to quantify their influence on the variability and quality of the optimal design. Results reveal a 33% difference in the optimal (final) weight between member group and profile list combinations producing the smallest and largest solution spaces. Yet, the lightest design (corresponding to the largest solution space) produced a greater number of unique steel shapes and required significantly higher computational cost. [ABSTRACT FROM AUTHOR]

Highlights: [•] Our approach allows designers to make a-posteriori tradeoffs between objectives. [•] Our robustness measures are the expected compliance and the expected variance. [•] Sizing is directly incorporated into the problem formulation. [•] We analyzed different sources of variability (force and material properties). [•] We obtained structures that were more robust than the deterministic approaches. [ABSTRACT FROM AUTHOR]

Performance of an optimization heuristic highly depends on the method it employs to represent and decompose the search space. A class of optimization methods, including swarm intelligence, utilizes a special way of decomposing the search space via specified direction states. The present work expands the idea, providing a stochastic directional state search with tuned thresholds for selecting every direction state. The proposed meta-heuristic is then utilized for the problem of structural weight minimization in building frames under lateral and gravitational loading using a finite element analyzer. Treating a number of examples from the literature, the efficiency and effectiveness of the proposed method are shown to be superior to some other meta-heuristics, including an improved particle swarm optimizer. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a sizing method and different control strategies for the suitable energy management of a stand-alone hybrid system based on photovoltaic (PV) solar panels, hydrogen subsystem and battery. The battery and hydrogen subsystem, which is composed of fuel cell (FC), electrolyzer and hydrogen storage tank, act as energy storage and support system. In order to efficiently utilize the energy sources integrated in the hybrid system, an appropriate sizing is necessary. In this paper, a new sizing method based on Simulink Design Optimization (SDO) of MATLAB was used to perform a technical optimization of the hybrid system components. An analysis cost has been also performed, in that the configuration under study has been compared with those integrating only batteries and only hydrogen system. The dynamic model of the designed hybrid system is detailed in this paper. The models, implemented in MATLAB-Simulink environment, have been designed from commercially available components. Three control strategies based on operating modes and combining technical-economic aspects are considered for the energy management of the hybrid system. They have been designed, primarily, to satisfy the load power demand and, secondarily, to maintain a certain level at the hydrogen tank (hydrogen energy reserve), and at the state of charge (SOC) of the battery bank to extend its life, taking into account also technical-economic analysis. Dynamic simulations were performed to evaluate the configuration, sizing and control strategies for the energy management of the hybrid system under study in this work. Simulation results show that the proposed hybrid system with the presented controls is able to provide the energy demanded by the loads, while maintaining a certain energy reserve in the storage sources. [Copyright &y& Elsevier]

Abstract: Harmony search (HS) algorithm was conceptualized using an analogy with music improvisation process where music players improvise the pitches of their instruments to obtain better harmony. Although the efficiency of HS algorithm has been proved in different engineering optimization applications, it is known that HS algorithm is quite sensitive to the tuning parameters. Several variants of HS algorithm have been developed to decrease the parameter-dependency character of HS algorithm. In this study, two improved harmony search algorithms called efficient harmony search algorithm (EHS) and self adaptive harmony search algorithm (SAHS) are proposed for sizing optimization of truss structures. Four classical truss structure weight minimization problems are presented to demonstrate the robustness of the proposed algorithms. The results of the present algorithms are compared with those of HS algorithm and other meta-heuristic algorithms recently developed in literature. [Copyright &y& Elsevier]

Abstract: Selection of the “best” or “optimum” engineering design has always been a major concern of designers. Reduction of hull weight is the most important aim in the structural design of many ship types. But the ability of designers to produce optimal designs of ship structures is severely limited by the calculation techniques available for this task. Complete definition of the optimal structural design requires formulation of size–topology–shape–material optimization task unifying optimization problems from four areas and effective solution of the problem. So far a significant progress towards solution of this problem has not been achieved. In other hand in recent years attempts have been made to apply genetic algorithm (GA) optimization techniques to design of ship structures. An objective of the paper was to create a computer code and investigate a possibility of simultaneous optimization of both topology and scantlings of structural elements of large spatial sections of ships using GA. In the paper GA is applied to solve the problem of weight minimization of a high speed vehicle-passenger catamaran structure with several design variables as dimensions of the plate thickness, longitudinal stiffeners and transverse frames and spacing between longitudinals and transversal members. Results of numerical experiments obtained using the code are presented. They show that GA can be an efficient optimization tool for simultaneous design of topology and sizing high speed craft structures. [Copyright &y& Elsevier]

This research investigates the optimization of a multifunctional structure with embedded electronic circuitry, following traditional composite laminate optimization methods. A heavily 'de-featured' finite element model provides thermal and mechanical analyses of the structure. The model places point heat sources at the surface component locations, and the optimization problem enforces strain constraints at these locations. A simple problem seeks the least-mass I-beam whose shear web contains a simple circuit, subject to strength and strain constraints. A second problem finds the lowest mass unmanned aerial vehicle (UAV) wing box configuration containing embedded circuitry subject to strength, deflection and strain constraints under two load cases. Sequential unconstrained minimization techniques and sequential quadratic programming perform the optimization; combinatorial methods are computationally impractical. Despite the model de-featuring and the use of calculus-based methods, the problem requires significant computational effort. The surface-component strain constraints result in structures with more mass than those without surface components. [ABSTRACT FROM AUTHOR]

Abstract: In discrete sizing optimization of truss and frame structures the design variables take values from databases, which are usually populated with a relatively small number of cross-section types and sizes. The aim of this work is to allow the use of large-size databases in discrete structural sizing optimization problems, in order to enrich the set of design variable options and increase the potential of achieving high-quality optimal designs. For this purpose, the concept of coarse database is introduced, according to which smaller-size versions of an appropriately ordered large database can be constructed. This concept is combined with the idea of cascading, which allows a single optimization problem to be tackled with a number of autonomous optimization stages. Under this context, several coarse versions of the same full-size database are formed, in order to utilize a different database in each cascade stage executed with an evolutionary optimization algorithm. The first optimization stages of the resulting multi-database cascade procedure make use of the coarsest database versions available and serve the purpose of basic design space exploration. The last stages exploit finer databases (including the original full-size database) and aim in fine tuning the achieved optimal solution. Based on the reported numerical results, multi-database cascading proves to be an effective tool for the handling of large databases and corresponding extensive design spaces in the framework of discrete structural sizing optimization applications. [Copyright &y& Elsevier]

This paper is concerned with design optimization of material reinforcement of automobile body for rigidity improvement. Two finite element optimization techniques, topology optimization and sizing optimization, are explored to find out an optimal manufacturing feasible design from multiple optimized designs. The application of these methods demonstrates that through innovative utilization of the topology optimization technique, an optimal manufacturing feasible design can be obtained. The relationship between the rigidity improvement and different configuration of material reinforcement is also investigated. It is concluded that through appropriate application of finite element optimization methods, the overall rigidity of the automobile body can be improved substantially in a cost effective manner. [Copyright &y& Elsevier]

[Display omitted] • Microgrid design optimized with Harris Hawks Optimization (HHO) algorithm achieves the lowest cost of energy and the lowest cost of investment. • With HHO, savings in total net present cost ranging from 1.18% to 18.23% are obtained compared to four well-known algorithms. • Sizing optimization with HHO yields a high rate of renewable energy factor and facilitates an eco-friendly microgrid design. • Developed energy management system achieves autonomous operation with reduced loss of power supply probability. In this study, the sizing optimization and design of an autonomous AC microgrid is performed using the Harris Hawks Optimization (HHO) algorithm. The objective is to demonstrate that more economical and reliable microgrids can be designed using the HHO algorithm. For this purpose, an autonomous AC grid is designed for the Gazi University Technology Park building located in Ankara. A yearlong energy consumption of the building and meteorological data have been measured and recorded. Based on the data, a microgrid structure consisting of photovoltaics (PV), wind turbines (WT), battery energy storage systems (BESS), and diesel generators (DG) is proposed. Finding the optimum capacities of these components is crucial to achieve the low cost and reliability objectives with true autonomous characteristics. On one hand oversizing that may arise from random assessments without any sizing optimization will increase the investment, operation, and maintenance costs. On the other hand, insufficient capacity planning based on prompt decisions can end up with problems in maintaining the energy continuity. Therefore, sizing optimization is a requirement for an effective microgrid design. In this context, after the sizing problem is solved using the HHO algorithm, the results are used in the design of the proposed microgrid. The simulation of the designed microgrid in MATLAB has demonstrated a successful performance providing savings ranging from 1.18% to 18.23% in the total net present cost (TNPC) value compared to other algorithms. The performance of the HHO algorithm is compared with four state-of-the-art metaheuristic algorithms, namely the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), the Gray Wolf Optimization (GWO), and the Salp Swarm Algorithm (SSA). Consequently, it is shown that the HHO algorithm has a competitive performance in design of cost effective and reliable microgrids. [ABSTRACT FROM AUTHOR]