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One of the core technologies in lightweight structures is the optimal design of laminated composite stiffened panels. The increasing tailoring potential of new materials added to the simultaneous optimization of various design regions, leading to design spaces that are vast and non-convex. In order to find an optimal design using limited information, this paper proposes a workflow consisting of design of experiments, metamodeling and optimization phases. A machine learning strategy based on support vector machine (SVM) is used for data classification and interpolation. The combination of mass minimization and buckling evaluation under combined load is handled by a multi-objective formulation. The choice of a deterministic algorithm for the optimization cycle accelerates the convergence towards an optimal design. The analysis of the Pareto frontier illustrates the compromise between conflicting objectives. As a result, a balance is found between the exploration of new design regions and the optimal design refinement. Numerical experiments evaluating the design of a representative upper skin wing panel are used to show the viability of the proposed methodology.

Abstract Currently, small islands are facing an energy supply shortage, which has led to considerable concern. Establishing an island microgrid is a relatively good solution to the problem. However, high investment costs restrict its application. In this paper, micro pumped storage (MPS) is used as an energy storage system (ESS) for islands with good geographical conditions, and deferrable appliance is treated as the virtual power source which can be used in the planning and operational processes. Household acceptance of demand response (DR) is indicated by the demand response participation degree (DRPD), and a sizing optimization model for considering the demand response of household appliances in an island microgrid is proposed. The particle swarm optimization (PSO) is used to obtain the optimal sizing of all major devices. In addition, the battery storage (BS) scheme is used as the control group. The results of case studies demonstrate that the proposed method is effective, and the DR of deferrable appliances and the application of MPS can significantly reduce island microgrid investment. Sensitivity analysis on the total load of the island and the water head of the MPS are conducted.

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 presents a damper design strategy for highly indeterminate 3D structures utilizing computational optimization and generalized response spectrum analysis, which is extended to incorporate non‐proportional damping. This enables a more efficient design process compared to trial‐and‐error with nonlinear response history analysis. The efficiency of the proposed design strategy is verified, analyzed and then applied to a series of illustrative examples including retrofit of an existing lattice tower structure and new build buckling‐restrained braced frames. [ABSTRACT FROM AUTHOR]

The implementation of a new linear method to optimum weight design of trussed structures subjected to external and self-weight loads is proposed. Design variables are the cross-section areas of the members. Inequality constraints are written based on the force-method for isostatic structures considering maximum and minimum axial stress criteria. The novelty of the proposed approach is the benefit created from the combination of a linear inequality-constrained formulation with interior-point methods to tunnel the solution rapidly and monotonically towards the minimum value through feasible space, also eliminating the need to directly explore the finite-element model. To evaluate the performance of the algorithm, trusses are subject to optimization processes based on different techniques: (i) the proposed method, called by "indirect-method"; (ii) a design problem with constraint evaluated directly from the finite-element model; (iii) optimization based on Genetic Algorithms. The three methods are compared using trusses with 10, 37 and 1240 bar-elements. The results showed that the indirect-method was able to provide great performance for complex topologies, returning weight designs up to 70 times lighter in 1% of the time required by a Genetic Algorithm. [ABSTRACT FROM AUTHOR]

This work explores the use of solid-shell elements in the the framework of isogeometric shape optimization of shells. The main difference of these elements with respect to pure shell ones is their volumetric nature which can provide recognized benefits to analyze, for example, structures with non-linear behaviors. From the design point of view, we show that this geometric representation of the thickness is also of great interest since it offers new possibilities: continuous sizing variations can be imposed by modifying the distance between the control points of the outer surfaces. In other words, shape and sizing optimization can be performed in an identical manner. Firstly, we carry out a range of numerical experiments in order to carefully compare the results with the commonly adopted technique based on the Kirchhoff-Love formulation. These studies reveal that both solid-shell and Kirchhoff-Love strategies lead to very similar optimal shapes. Then, we apply a bi-step strategy to integrate shape and sizing optimization. We highlight the potential of the proposed approach on a stiffened cylinder where the cross section along the stiffener is optimized leading to a final design with smooth thickness variations. Finally, we combine the benefits of both Kirchhoff-Love and solid-shell formulations by setting up a multi-model optimization process to efficiently design a roof. [ABSTRACT FROM AUTHOR]

A novel optimization strategy is proposed to achieve a reliable hybrid plant of wind, solar, and battery (HWSPS). This strategy’s purpose is to reduce the power losses in a wind farm and at the same time reduce the fluctuations in the output of HWSPS generation. In addition, the proposed strategy is different from previous studies in that it does not involve a load demand profile. The process of defining the HWSPS capacity is carried out in two main stages. In the first stage, an optimal wind farm is determined using the genetic algorithm subject to site dimensions and spacing between the turbines, taking Jensen’s wake effect model into consideration to eliminate the power losses due to the wind turbines’ layout. In the second stage, a numerical iterative algorithm is deployed to get the optimal combination of photovoltaic and energy storage system sizes in the search space based on the wind reference power generated by the moving average. The reliability indices and cost are the basis for obtaining the optimal combination of photovoltaic and energy storage system according to a contribution factor with 100 different configurations. A case study in Thumrait in the Sultanate of Oman is used to verify the usefulness of the proposed optimal sizing approach.

Highlights • Strategy to progressively optimize the sizing design of solar panels and batteries is proposed. • No high initial investment is required. • Bank loans and time-dependent parameters/prices are considered. Abstract In many locations, grids are energized only by diesel generators (DG-only systems). Due to the requirements such as cost reduction and performance improvement, solar panels and batteries have increasingly been introduced into these grids. Nonetheless, many initiatives to hybridize the grids are still thwarted because of the high initial investment. In this work, a gradual diesel replacement strategy is proposed. The strategy starts with a lower initial investment and gradually adds solar panels and batteries in the subsequent years using accumulated savings from the lower diesel consumption. An Indonesian island is used as a case study to validate the proposed strategy. When only diesel generators are used, the life-cycle cost over a period of 25 years is 10.53 mil. USD. It is demonstrated through optimization that by lowering the initial investment to 0.25 mil. USD and gradually using the yearly savings to reinvest in purchasing additional solar panels and batteries, the life-cycle cost is reduced to 6.62 mil. USD (only 3.8% is required for the initial investment). Hence, with the proposed approach, the barrier of initial investment is minimized, and thus more renewable energy sources can be integrated into existing DG-only systems making the power generation more environment-friendly. [ABSTRACT FROM AUTHOR]

In this paper, we propose a working-set approach for sizing optimization of structures subjected to time-dependent loads. The optimization problems we consider have a very large number of constraints while relatively few design variables and degrees of freedom. Instead of solving the original problem directly, we solve a sequence of smaller sub-problems. The sub-problems consider only constraints in the working set, which is a small sub-set of all constraints. After each sub-problem, we compute all constraint function values for the current design and add critical constraints to the working set. The algorithm terminates once an optimal point to a sub-problem is found that satisfies all constraints of the original problem. We tested the approach on several reproducible problem instances and demonstrate that the approach finds optimal points to the original problem by only considering a very small fraction of all constraints. The proposed approach drastically reduces the memory storage requirements and computational expenses of the linear algebra in the optimization solver and the computational cost of the design sensitivity analysis. Consequently, the approach can efficiently solve large-scale optimization problems with several hundred millions of constraints. [ABSTRACT FROM AUTHOR]

The Pontryagin's minimum principle is utilized in this paper to determine the best solution of component sizing and energy management strategy for a plug-in hybrid electric vehicle which is equipped with a hybrid energy storage system. The hybrid energy storage system, including batteries and supercapacitors, is an effective solution to extend battery life span and reduce the vehicle operating cost. The operating costs of different hybrid energy storage system candidates, including fuel cost, electricity cost, and battery degradation cost over 6 consecutive China bus driving cycles, are minimized by using a 2-dimensional Pontryagin's minimum principle algorithm proposed in this paper. The proposed Pontryagin's minimum principle algorithm not only determines the optimal energy management strategy, but also globally finds the optimal battery and supercapacitor sizes. It is shown that the operating cost strictly decreases with increasing battery and supercapacitor sizes. In addition, simulation results show that the operating cost is reduced by up to 28.6% when compared to a conventional hybrid powertrain without supercapacitors. Thus the effectiveness of adopting supercapacitors in plug-in hybrid electric vehicles is verified. [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]

An extensive utilization of wireless sensor networks has evolved during the last years for monitoring various environmental and artificial processes. When operating in remote locations, the nodes of wireless sensor networks are typically power supplied by an energy production and management system, comprising low-power renewable energy sources, a power electronic converter, and a battery-based energy storage unit. In this paper, a methodology is proposed for optimally designing the energy production and processing system of a wireless sensor network node simultaneously at both the renewable power-supply system level and the power converter circuit level, through a unified design process. The impact of the objective function type on the power-supply design is also investigated in this paper. Design optimization and experimental results are presented, which demonstrate that the optimized power-supply structures derived by applying the proposed optimization technique exhibit lower cost of generated energy compared to partially optimized or totally nonoptimized structures and by that reduce the cost of the overall wireless sensor network node. [ABSTRACT FROM PUBLISHER]

The development of off-grid hybrid renewable energy systems (HRESs) is essential to rural electrification and global decarbonization. Based on 299 journal papers in the recent five years, this work conducts a state-of-the-art qualitative review and quantitative bibliometric analysis on the sizing optimization of off-grid HRESs. An overview of system configurations, energy management strategies, performance evaluation indicators, and sizing methodologies are presented, and bibliometric analysis is conducted to reveal the overall scope and mainstream of this research field. Finally, promising future works are summarized on the basis of current research gaps. The results of bibliometric analysis indicate that: (1) solar photovoltaic and batteries are the most common energy source and energy storage respectively, and wind-photovoltaic-battery-diesel is the most popular system configuration; (2) most researchers apply rule-based energy management strategies rather than optimized strategies, owing to their advantages of simple implementation and fast computation; (3) 97.99% of articles considers economic indicators in the sizing optimization model, and techno-economic feasibility is the essential research foundation at the planning stage; (4) meta-heuristic algorithms and the HOMER software tool are the two most popular sizing methodologies for off-grid HRESs. In future works, hybrid energy storage systems, deep reinforcement learning-based energy management strategy, sustainability and resilience indicators, as well as distributionally robust optimization-based sizing methodologies are promising research directions. Overall, the presented overviews and outlooks can provide holistic theoretical knowledge about sizing optimization research for practitioners, thus promoting the academic progress and practical implementation of off-grid HRESs. • Qualitative review and quantitative bibliometric analysis on energy system planning. • Overview of conventional and emerging renewable energy and storage technologies. • Classification of energy management strategies integrated with sizing optimization. • Multi-perspective projection of future works based on current knowledge gaps. [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]

This paper discusses the design process of a generator to utilize waste diesel engines in the automotive industry, and presents the results of the design, analysis, and test evaluation of a 78 kW permanent magnet synchronous generator. In the initial design process using the finite-element analysis and space harmonic analysis, the number of poles and slots was determined. Then, the generator characteristics were investigated by the torque per rotor unit volume method and the $d$ , $q$ -axis equivalent circuit. Last, the optimal size of the generator was determined. This process makes it possible to design in consideration of efficiency, inductance, material cost, and temperature characteristics. A prototype of the model was created and tested with uncertainty analysis, and a comparison between the experimental results and the results of the optimization was conducted to validate the analytic approach. [ABSTRACT FROM AUTHOR]

Truss layout optimization is a procedure for optimizing truss structures under the combined influence of size, shape and topology variables. This paper presents an Improved Genetic Algorithm with Two-Level Approximation (IGATA) that uses continuous shape variables and shape sensitivities to minimize the weight of trusses under static or dynamic constraints. A uniform optimization model including continuous size/shape variables and discrete topology variables is established. With the introduction of shape sensitivities, the first-level approximations of constraint functions are constructed with respect to shape/topology/size variables. This explicit problem is solved by implementation of a real-coded GA for continuous shape variables and binary-coded GA for 0/1 topology variables. Acceleration techniques are used to overcome the convergence difficulty of the mixed-coded GA. When calculating the fitness value of each member in the current generation, a second-level approximation method is embedded to optimize the continuous size variables effectively. The results of numerical examples show that the usage of continuous shape variables and shape sensitivities improves the algorithm performance significantly. [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]

A new design methodology, which evaluates the optimum configuration of network arch bridge schemes is proposed. A three-step optimization algorithm is implemented in a FE model, with the purpose to evaluate the bridge optimum configuration, involving the lowest material quantity and the best strength performance level in all structural members of the bridge. The stability and the efficiency of the formulation were verified with respect to several bridge configurations ranging from small to large spans. Moreover, parametric results are presented to investigate the interaction between cable-system, girder and arch, giving rise to specific analyses useful for design purposes. [ABSTRACT FROM AUTHOR]

Stand-alone hybrid photovoltaic(PV)-wind-diesel-battery system is becoming an appropriate choice of power supply system for remote areas far from the power grid. However, the sizing optimization of the stand-alone hybrid PV-wind-diesel-battery system is difficult because of the system's complexity. In this paper, a novel improved fruit fly optimization algorithm-based multi-objective optimization method is proposed for the optimization design of this system. Here, the objectives to be minimized are the annual total cost and the pollutant emission of the system. The obtained solutions of the best Pareto front can help decision-makers to choose the prior one. The simulation conducted in this paper is based on real data collected from Dongao Island. Simulation results show the excellent properties of the proposed optimization method and demonstrate the feasibility of the stand-alone hybrid PV-wind-diesel-battery system in Dongao Island. [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]

This review presents developed models, theory, and numerical methods for structural optimization of trusses with discrete design variables in the period 1968 - 2014. The comprehensive reference list collects, for the first time, the articles in the field presenting deterministic optimization methods and meta heuristics. The field has experienced a shift in focus from deterministic methods to meta heuristics, i.e. stochastic search methods. Based on the reported numerical results it is however not possible to conclude that this shift has improved the competences to solve application relevant problems. This, and other, observations lead to a set of recommended research tasks and objectives to bring the field forward. The development of a publicly available benchmark library is urgently needed to support development and assessment of existing and new heuristics and methods. Combined with this effort, it is recommended that the field begins to use modern methods such as performance profiles for fair and accurate comparison of optimization methods. Finally, theoretical results are rare in this field. This means that most recent methods and heuristics are not supported by mathematical theory. The field should therefore re-focus on theoretical issues such as problem analysis and convergence properties of new methods. [ABSTRACT FROM AUTHOR]

• A new structure of off-grid integrated energy system (OIES) for an isolated renewable energy hydrogen refueling station (HRS) is proposed. • A mixed integer quadratic constrained programming (MIQCP) model of the OIES is built. • These three loads are obtained by using the authoritative building model to simulate the operation in EnergyPlus software. • The optimal sizing and scheduling results and the sensitivity analysis results are presented numerically and visually. When designing an isolated renewable energy hydrogen refueling station (HRS), it is important to consider the electrical, heating and cooling demands of the supporting building as it is crucial for maintaining the stable HRS operation. Therefore, this paper proposed a new structure of off-grid integrated energy system (OIES) for an isolated renewable energy HRS, which can meet the hydrogen load of the market and the three kinds of loads of the supporting building. The OIES in question consists of wind generators, PV panels, batteries, electrolyzers, heat storage tanks, hydrogen tanks, and absorption chillers. A mixed integer quadratic constrained programming (MIQCP) model of the OIES is built based on the minimization of the total life cycle cost (TLCC). It is built to obtain the optimal solution for sizing and the equipment units' scheduling strategy. To accord with the actual situation, the electrical, heating, and cooling loads of the supporting building in the simulation are obtained by the EnergyPlus software. Furthermore, the 'community' hydrogen demand is obtained by the HOMER software. The results indicate that the proposed structure meets a wide array of energy demands and is more sensible solution when compared to other configurations with one component less. In addition, sensitivity analyses of the influence of economic data, meteorological data, and hydrogen load on TLCC are performed. These results show that the capital and replacement cost of the PV panels are the major economic factors influencing the TLCC. They also confirm that the PV is the system's main component due to a high correlation between the hourly hydrogen demand per year and the hourly solar radiation per year. Lastly, the effects of meteorological data and hydrogen load are also quantitatively analyzed. [ABSTRACT FROM AUTHOR]

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]

In this paper we address the optimal sizing and scheduling of isolated hybrid systems using an optimization framework. The hybrid system features wind and photovoltaic conversion systems, batteries and diesel backup generators to supply electricity demand. A Mixed-Integer Linear Programming formulation is used to model system behavior over a time horizon of one year, considering hourly changes in both the availability of renewable resources and energy demand. The optimal solution is achieved with respect to the minimization of the levelized cost of energy (LCOE) over a lifetime of 20 years. Results for a case study show that the most economical solution features all four postulated subsystems. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) have been widely identified as an efficient mechanism to generate electrical power based on renewable energy sources (RES). This kind of energy generation systems are based on the combination of one or more RES allowing to complement the weaknesses of one with strengths of another and, therefore, reducing installation costs with an optimized installation. To do so, optimization methodologies are a trendy mechanism because they allow attaining optimal solutions given a certain set of input parameters and variables. This work is focused on the optimal sizing of hybrid grid-connected photovoltaic–wind power systems from real hourly wind and solar irradiation data and electricity demand from a certain location. The proposed methodology is capable of finding the sizing that leads to a minimum life cycle cost of the system while matching the electricity supply with the local demand. In the present article, the methodology is tested by means of a case study in which the actual hourly electricity retail and market prices have been implemented to obtain realistic estimations of life cycle costs and benefits. A sensitivity analysis that allows detecting to which variables the system is more sensitive has also been performed. Results presented show that the model responds well to changes in the input parameters and variables while providing trustworthy sizing solutions. According to these results, a grid-connected HRES consisting of photovoltaic (PV) and wind power technologies would be economically profitable in the studied rural township in the Mediterranean climate region of central Catalonia (Spain), being the system paid off after 18 years of operation out of 25 years of system lifetime. Although the annual costs of the system are notably lower compared with the cost of electricity purchase, which is the current alternative, a significant upfront investment of over $10 M – roughly two thirds of total system lifetime cost – would be required to install such system. [ABSTRACT FROM AUTHOR]

Hybrid renewable energy systems (HRES) are a trendy alternative to enhance the renewable energy deployment worldwide. They effectively take advantage of scalability and flexibility of these energy sources, since combining two or more allows counteracting the weaknesses of a stochastic renewable energy source with the strengths of another or with the predictability of a non-renewable energy source. This work presents an optimization methodology for minimum life cycle cost of a HRES based on solar photovoltaic, wind and biomass power. Biomass power seeks to take advantage of locally available forest wood biomass in the form of wood chips to provide energy in periods when the PV and wind power generated are not enough to match the existing demand. The results show that a HRES combining the selected three sources of renewable energy could be installed in a rural township of about 1300 dwellings with an up-front investment of US $7.4 million, with a total life cycle cost of slightly more than US $30 million. Such a system would have benefits in terms of energy autonomy and environment quality improvement, as well as in term of job opportunity creation. [ABSTRACT FROM AUTHOR]

Recently a design-driven heuristic approach named guided stochastic search (GSS) technique has been developed by the authors as a computationally efficient method for discrete sizing optimization of steel trusses. In this study, an extension and reformulation of the GSS technique are proposed for its application to problems from discrete sizing optimization of steel frames. In the GSS, the well-known principle of virtual work as well as the information attained in the structural analysis and design stages are used together to guide the optimization process. A design wise strategy is employed in the technique where resizing of members is performed with respect to their role in satisfying strength and displacement constraints. The performance of the GSS is investigated through optimum design of four steel frame structures according to AISC-LRFD specifications. The numerical results obtained demonstrate that the GSS can be employed as a computationally efficient design optimization tool for practical sizing optimization of steel frames. [ABSTRACT FROM AUTHOR]

In the present study a new metaheuristic algorithm called adaptive dimensional search (ADS) is proposed for discrete truss sizing optimization problems. The robustness of the ADS lies in the idea of updating search dimensionality ratio (SDR) parameter online during the search for a rapid and reliable convergence towards the optimum. In addition, several alternative stagnation-control strategies are integrated with the algorithm to escape from local optima, in which a limited uphill (non-improving) move is permitted when a stagnation state is detected in the course of optimization. Besides a remarkable computational efficiency, the ease of implementation and capability of locating promising solutions for challenging instances of practical design optimization are amongst the remarkable features of the proposed algorithm. The efficiency of the ADS is investigated and verified using two benchmark examples as well as three real-world problems of discrete sizing truss optimization. A comparison of the numerical results obtained using the ADS with those of other metaheuristic techniques indicates that the proposed algorithm is capable of locating improved solutions using much lesser computational effort. [ABSTRACT FROM AUTHOR]

The economic benefit gained from a combined cooling heating and power (CCHP) system depends on many factors. It is essential to optimize the system configuration and operational scheme as early in the design stage as possible. In this paper, a sizing optimization model (SOM) focused on the minimum annual total cost ( ATC ) and an operational optimization model (OOM) focused on the minimum annual energy charge ( AEC ) were established. The former was used to determine the optimal capacity of a CCHP system, whereas the latter was used to provide the optimal schemes. Both the SOM and OOM contain large numbers of hourly linear programming (LP) models, and the solution processes of both models proved to be quite time-consuming when the conventional iterative method was used. In this study, a piecewise elimination method was proposed for hourly LP models, and a graphical method in which the iteration was converted to an algebraic matrix was introduced to greatly improve the computational efficiency. The results demonstrated that the calculation time was reduced from 84.496 s for the conventional solution to 0.017 s for the graphical method in the OOM. The CCHP system SOM contains an OOM, and the calculation time was only 0.41 s when using the graphical method, whereas the conventional iterative method took 2367 s. The impact of gas price upon the optimal operational schemes and the equipment capacity was discussed for a building application in Shanghai. The improved efficiency of the calculation method could be helpful to compare different schemes and to perform sensitivity analyses. [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]