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Graphical abstract Material Handling System. Highlights • A hybrid multi-objective optimization algorithm derived from immunological and biological evolution concepts is proposed. • An optimization approach integrating the proposed algorithm with an industrial-grade simulator is implemented. • The optimization approach’s performance is examined via numerical benchmark problems and a real-life simulation study. • The results reveal its ability of helping management to find near optimal system operating conditions and parameters. Abstract Optimization of complex real-world problems often involves multiple objectives to be considered simultaneously. These objectives are often non-commensurable and competing. For example, material handling is a vital element of industrial processes, which involves a variety of operations including the movement, storage and control of materials throughout the processes of manufacturing, distribution, and disposal while having to satisfy multiple objectives. Having an efficient and effective material handling system (MHS) is of great importance to various industries, such as manufacturing and logistics industries, for maintaining and facilitating a continuous flow of materials through the workplace and guaranteeing that required materials are available when needed. In this paper, a hybrid multi-objective optimization algorithm largely based on Artificial Immune Systems (AIS) is applied to simulation-based optimization of material handling system. This proposed algorithm hybridizes the AIS with the Genetic Algorithm (GA) by incorporating the crossover operator derived from the biological evolution. The reason behind such hybridization is to further enhance the diversity of the clone population and the convergence of the algorithm. In this paper, other than conducting numerical experiments to assess the performance of the proposed algorithm by using several benchmark problems, the proposed algorithm is also applied to optimize a multi-objective simulation-based problem on a material handling system in order to demonstrate the applicability of the proposed algorithm in real-life cases. The results show that for most cases the proposed algorithm outperforms the other benchmark algorithms especially in terms of solution diversity. [ABSTRACT FROM AUTHOR]

A new class of wind turbine termed the external axis wind turbine (EAWT) has been recently developed in response to the anticipated increase in global demand for renewable energy. The EAWT combines the high power output of the HAWT with the low installation and maintenance cost of the VAWT. This paper optimizes this EAWT to simultaneously maximize the power while minimizing power fluctuation and the time required to reach the optimal operating point. The multi-objective optimization on the EAWT using Genetic Algorithm is performed on a response surface that is generated using CFD simulations in OpenFOAM. The turbulence model and mesh-sizing for the CFD simulations are validated against previously published experimental results on a bluff body. The paper first optimizes the EAWT using steady state CFD simulations which are found to be not valid. The paper then optimizes the EAWT using transient CFD simulation with moving mesh, first for a single Reynolds number then for a wide range of Reynolds numbers. From the analysis, a blade count of 10 at an operational tip speed ratio of 0.32 is recommended. This paper is the first study on EAWT. To the best of authors' knowledge, this paper is also the first study on wind-turbines to optimize changing blade count and operating point to simultaneously maximize power, while minimizing power fluctuation and the time needed to reach peak operating point. [ABSTRACT FROM AUTHOR]

In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional multi-objective optimization method whose aim is to find a single Pareto solution, MOGA tends to find a representation of the whole Pareto frontier. During the process of solving multi-objective optimization problems using genetic algorithm, one needs to synthetically consider the fitness, diversity and elitism of solutions. In this paper, more specifically, the optimal sequence method is altered to evaluate the fitness; cell-based density and Pareto-based ranking are combined to achieve diversity; and the elitism of solutions is maintained by greedy selection. To compare the proposed method with others, a numerical performance evaluation system is developed. We test the proposed method by some well known multi-objective benchmarks and compare its results with other MOGASs׳; the result show that the proposed method is robust and efficient. [ABSTRACT FROM AUTHOR]

Highlights • A heuristic search strategy was proposed for optimal learning group formation. • NSGA-II, a multi-objective optimization tool, was utilized for optimal grouping. • It was capable of enhancing inter-group homogeneity and Intra-group Heterogeneity. • Efficient and reliable fitness functions were defined to stop iterations. • Various characteristics of any data types and range of variations can be handled. Abstract In modern education systems, plenty of research suggests that clustering the learners into optimal learning groups based on their multiple characteristics is a determining effort in enhancing the effectiveness of collaborative learning. Although there have been several evidences on developing and implementing appropriate computational tools to handle classification processes in expert and intelligent systems, the effectiveness and accuracy of optimal grouping algorithms are still worth improving. For instance, the majority of grouping processes in collaborative learning environments is orchestrated through single-objective optimization algorithms, which need to be revisited due to some intrinsic limitations. In this paper, we propose a novel algorithm capable of properly addressing a variety of optimization problems in optimal learning group formation processes. To this end, a multi-objective version of Genetic Algorithms, i.e. Non-dominated Sorting Genetic Algorithm, NSGA-II, was successfully implemented and applied to improve the performance and accuracy of optimally formed learning groups. In contrast to the previous related works applying single-objective algorithms, the main advantage of our work is simultaneous satisfaction of multiple targets predefined for the formation of optimal learning groups, especially the inter-homogeneity and intra-heterogeneity of each learning group, which significantly enhance both effectiveness and accuracy of optimal grouping processes in the underlying intelligent systems. Challenging the proposed optimization algorithms, both single- and multi-objective optimizers, with a similar grouping problem, clearly proved that the single-objective optimization technique has limited control and sensitivity to the quality of individual groups. Contrary to single-objective optimization techniques, which are mainly governed by adjusting the quality of the groups altogether in average, the proposed multi-objective algorithm not only takes the average desirability of all formed groups into account but also precisely monitors the fitness of each group in a potential solution distinctively. The generality of the proposed algorithm makes it a suitable candidate not only to handle optimal grouping in learning environments but also to be competent enough for grouping problems in other domains as well. [ABSTRACT FROM AUTHOR]

Abstract In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view. [ABSTRACT FROM AUTHOR]

Abstract The existing evolutionary algorithm techniques have limited capabilities in solving large-scale combinatorial problems due to their large search space, making impractical the examination of big real-world instances. In this paper, we address this issue by introducing a new algorithm that incorporates a coding structure specially designed to keep the processing time invariant to the size of the examined test instance, allowing the consideration of large-scale problems for a fraction of time required by other techniques. We test the performance of the proposed algorithm to the optimal allocation of limited resources to a number of competing investment opportunities for optimizing the objectives. We believe that the proposed algorithm can be particularly useful in other contexts too, subject to adaptations relevant to specific problem requirements. Highlights • Existing techniques have limited capabilities in solving large combinatorial problems. • The proposed algorithm keeps the processing time invariant to the problem's size. • It is tested to optimal allocation of limited resources to a number of investments. • It outperforms the other techniques in terms of performance and computational time. • It can be proved very useful in problems with large number of alternative choices. [ABSTRACT FROM AUTHOR]

Evolutionary optimization algorithms, a meta-heuristic approach, often encounter considerable challenges in many-objective optimization problems (MaOPs). The Pareto-based dominance loses its effectiveness in MaOPs, which are defined as having more than three objectives. Therefore, a more valid selection method is proposed to balance convergence and distribution. This paper proposes an algorithm using rotary grid technology to solve MaOPs (denoted by RGridEA). The algorithm uses the rotating grid to partition the objective space. Instead of using the Pareto non-dominated sorting strategy to layer the population a novel stratified method is used to enhance convergence effectively and make use of the grid to improve distribution and uniformity. Finally, with the other seven algorithm was tested on the test function DTLZ series analysis, confirming RGridEA is effective in resolving MaOPs. [ABSTRACT FROM AUTHOR]

The primary task of a memory-based collaborative filtering (CF) recommendation system is to select a group of nearest (similar) user neighbors for an active user. Traditional memory-based CF schemes tend to only focus on improving as much as possible the accuracy by recommending familiar items ( i.e ., popular items over the group). Yet, this may reduce the number of items that could be recommended and consequently weakens the chances of recommending novel items. To address this problem, it is desirable to consider recommendation coverage when selecting the appropriate group. This could help in simultaneously making both accurate and diverse recommendations. In this paper, we propose to focus mainly on the growing of the large search space of users’ profiles and to use an evolutionary multi-objective optimization-based recommendation system to pull up a group of profiles that maximizes both similarity with the active user and diversity between its members. In such manner, the recommendation system will provide high performances in terms of both accuracy and diversity. The experimental results on the Movielens benchmark and on a real-world insurance dataset show the efficiency of our approach in terms of accuracy and diversity compared to state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

In this paper, we propose an original double flexible job-shop scheduling problem (DFJSP), in which both workers and machines are flexible. Because of the characteristics of double flexibility, DFJSP conforms to practical production better than the flexible job-shop scheduling problem (FJSP). In addition, a multi-objective optimization mathematic model according to the DFJSP is proposed, which is concerned with the processing time indicator that is usually optimized by most existing studies; green production indicators, namely, factors regarding environmental protection; and human factor indicators, which are actual indispensable elements that exist in the production system. Furthermore, ten benchmarks of DFJSP are presented and solved using a newly proposed hybrid genetic algorithm (NHGA). With the proposed NHGA, a new well-designed three-layer chromosome encoding method and some effective crossover and mutation operators are developed. To obtain the best combination of key parameters in NHGA, the Taguchi design of experiment method is used for their evaluation. Finally, comparisons between NHGA and NSGA-II show that the proposed NHGA has advantages in terms of the solving accuracy and efficiency of the DFJSP, particularly at a large scale. It would be beneficial to apply our proposed model to the multi-objective optimization of scheduling problems, especially those considering human factor and green production-related indicators. [ABSTRACT FROM AUTHOR]

In the last years, multi-objective optimization techniques became into one of the main challenges of the building energy efficiency area. The objective of this paper is to develop and validate a computational code for multi-objective building performance optimization by linking an evolutionary algorithm and a building simulation software in a powerful cluster. A sophisticated version of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was implemented in Python code to determine the optimal building design, which allows working with categorical and discrete variables, and the objectives were evaluated using the building energy simulation software EnergyPlus. NSGA-II was implemented to run in a high-performance cluster for the parallel computing of the fitness of each population (set of possible designs). In this work, the strengths of the proposed method were demonstrated by its application to the optimal design of a typical single-family house, located in the Argentine Littoral region. This house has some rooms conditioned only by natural ventilation, and other rooms with natural ventilation supplemented by mechanical air-conditioning (hybrid ventilation). The most influential design variables like roof types, external and internal wall types, solar orientation, solar absorptance, size, type, and windows shading of this house among others were studied in two complex cases of 10 8 and 10 16 possibilities to obtain the best trade-off (Pareto front) between heating and cooling performance. Finally, a decision-making method was applied to select one configuration of the Pareto front. Optimal simulation results for the study cases indicated that is possible to improve up to 95% the thermal comfort in naturally ventilated rooms and up to 82% energy performance in air-conditioned rooms of the building with respect to the original configuration by using a design that takes simultaneous advantage of passive strategies like thermal inertia and natural ventilation. The methodology was proved to give a robust and powerful tool to design efficient dwellings reducing the optimization time from almost 12 days to 4.4 h. [ABSTRACT FROM AUTHOR]

Multi-objective optimization of detailed building models may lead to high computational time expenses. The work presented in this paper presents an alternative of classical approaches based on the usage of reduced sequences in a sequential and an adaptive approach. The sequential approach runs directly the reduced sequence obtained from a typical day selection algorithm which employs a k- medoids clustering algorithm in a multi-objective optimization study. It was applied on a solar combisystem connected to a building model and showed its capability of reproducing the Pareto front 25 times faster with relative errors not exceeding 5% when considering several parametric configurations of the model and focusing on a limited number of selection criteria during the day selection process. On the other hand, the adaptive approach for optimization named OptiTypSS achieves day selection during the optimization process by combining an iterative day selection algorithm (TypSS) with a genetic algorithm of optimization (NSGA-II). The method succeeded in obtaining results with errors inferior to 3% being therefore as efficient as a classical metamodel adaptive approach yet with relatively higher computational time. Further improvements including parallel simulation during the day selection process can be done to enhance the performance. • Optimization of a solar combi system model using short simulation sequences in a sequential and an adaptive approach. • In the sequential approach, the optimization algorithm converged 25 times faster with relative errors of a maximum of 5%. • In the adaptive approach, the Pareto front superposed the reference one recording relative errors inferior to 2.5%. • Results obtained by the adaptive approach were as efficient as using metamodels with relatively higher computational time. [ABSTRACT FROM AUTHOR]

The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi-period dynamic emergency resource scheduling (ERS) problems. Then, using the framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an MOEA is proposed to solve this model. In the proposed algorithm, new evolutionary operators are designed with the intrinsic properties of multi-period dynamic ERS problems in mind. The experimental results show that the proposed algorithm can get a set of better candidate solutions than the non-dominated sorting genetic algorithm II (NSGA-II). [ABSTRACT FROM AUTHOR]

For multi-objective design of multi-parameter antenna structures, optimization efficiency and computational cost are two major concerns. In this paper, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed to improve global optimization capability by diversity detection operation and mixed population update operation. Further, in order to reduce the computational cost, a hybrid optimization strategy integrating a dynamically updatable surrogate-assisted model into the improved MOEA/D is proposed. The numerical results of test functions show that our algorithm outperforms original MOEA/D, modified MOEA/D (M-MOEA/D), and nondominated sorting genetic algorithm II (NGSA-II) in terms of diversity. Experimental validation of Pareto-optimal planar miniaturized multiband antenna designs is also provided, showing excellent convergence and considerable computational savings compared to those previously published approaches. [ABSTRACT FROM AUTHOR]

Maintenance planning is a critical issue for all heavy industrial sectors such as aeronautics, automobile factories and power plants. The study herein is aimed towards improvement of safety systems reliability in a nuclear power plant. Optimization of maintenance and surveillance test activities is one of the best strategies to improve the reliability of the related safety systems in this kind of plants. Maintenance programs can be formulated in terms of a multi-objective optimization where unavailability, cost and Exposure Time (ET) act as decision criteria and surveillance tests, Allowed Outage Time (AOT) and Preventive Maintenance (PM) intervals act as decision variables. In this paper, Genetic Algorithm (GA) is used to find the best series of answers in the form of Pareto front curve. Sensitivity Index (SI) is applied as a decision making tool to extract the most optimized and promising solution. The unavailability, cost and ET functions, for the most optimal solutions, were reduced by 86%, 58% and 30%, respectively. [ABSTRACT FROM AUTHOR]

In flotation process, the operating conditions under which the concentrate grade is high are the same conditions that lead to a lower recovery and selectivity. Thus, in the face of contradictory, but inherent characteristics of flotation phenomenon, it is necessary to apply appropriate techniques of multi-objective optimization in order to meet specific needs. In the present paper the Firefly Algorithm, associated with the Pareto dominance criterion and with an anti-stagnation operator, is used to determine the best operating conditions of the column flotation of an igneous apatite ore. The aim is to find the conditions that lead to a maximum P 2 O 5 grade and recovery maximization, with adequate selectivity. The results indicated that the proposed approach was able to obtain the operating conditions which led to the best performances for this flotation process. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid method for simultaneously maximizing fundamental natural frequency and thermal buckling temperature of laminated composite plates is developed. This method is a new combination of the differential quadrature method (DQM), non-dominated sorting genetic algorithm II (NSGA-II) and Young bargaining model. The governing equations are obtained within the framework of the first-order shear deformation theory (FSDT) of plates and are discretized using the DQM. Then, the DQM is linked with the NSGA-II optimization model and the trade-off between the objectives with respect to fibers orientations is obtained. Finally, by applying Young bargaining model the best fibers orientations which maximize the objectives of laminated composite plates with different boundary conditions, thickness-to-length and aspect ratios are obtained. [ABSTRACT FROM AUTHOR]

This paper presents a novel model for multistage smart distribution network expansion planning (MSDNEP) in the presence of vehicle to grid (V2G) and fault passage indicator (FPI) in a multi-objective optimization framework. Distribution networks should be expanded in order to serve load growth in the best manner possible. The two objectives considered are: minimization of total cost during the planning horizon and maximization of reliability index. The proposed model determines the sitting and sizing of parking lot as well as conventional alternatives for expansion, such as, optimal size, location and time in which the new lines must be added, replaced or removed. Moreover, the impacts of using FPIs in a distribution network on reliability and the investment cost are in the scope of this study. Due to its good handling of mixed integer nonlinear problem, a non-dominated sorting genetic algorithm (NSGA-II) is utilized for optimization such a complex problem. The effectiveness of the proposed model is demonstrated by applying to a distribution test system and results show a significant improvement in the area of distribution network planning. [ABSTRACT FROM AUTHOR]

Connection frame is an essential component to implement high acceleration and ultra-precision positioning motion in a macro–micro motion platform. The performance of the positioning system is mainly affected by two sources which include thermal–mechanical deformation and the natural frequency of connection frame. In the paper, multi-objective optimization and design for the connection frame is constructed and discussed comprehensively by the effects of thermal–mechanical deformation and the natural frequency of the system. The optimization objectives for the connection structure are the minimized displacement when thermal–mechanical deformation is occurred, the maximized natural frequency to avoid system resonance, and the light weight for the connection structure to fulfil high acceleration motion. Using response surface method (RSM) combined with finite element method (FEM), the objective function is formulated as a prediction model. Non-dominated Sorting Genetic Algorithm II (NSGAII) is used to solve the optimization model and attain the matched parameters. A cantilever beam example is tested to examine the validity of the methodology, and the results from prediction model agree well with that from theoretical model. By the above methodology, a high performance with optimal parameters for the connection structure is obtained, and its natural frequency and weight can meet our design expectation. [ABSTRACT FROM AUTHOR]

The recast version of the Energy Performance of Buildings Directive (2010/31/EU) proposes a comparative methodology aimed at defining the energy performance of buildings “with a view to achieving cost-optimal levels”. Really, how can the cost-optimal technologies be detected? Moreover, how can the most proper packages of energy efficiency measures be chosen in order to obtain the cost-optimality? This paper would provide answers to these questions, by proposing a new methodology for the evaluation of the cost-optimality, by means of the multi-objective optimization of energy performance of buildings and indoor thermal comfort. The optimization procedure is developed by means of the coupling between MatLab and EnergyPlus, by implementing a genetic algorithm, and it allows the evaluation of profitable and feasible packages of energy efficiency measures applied to buildings. Then, following the adoption of these packages, the global cost over the lifecycle of the building is calculated, in order to identify the cost-optimal solution. After the presentation of the methodology, the developed method is proposed for the optimization of the energy retrofit of an existing building. Furthermore, the method can be applied also for new architectures, by considering reference buildings. [ABSTRACT FROM AUTHOR]

Retrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs. [ABSTRACT FROM AUTHOR]

In this paper, general strategies are presented by which a multi-domain, multi-objective, mechanism-based optimization problem may be efficiently formulated and solved by means of a genetic algorithm. These strategies include integration of traditional precision position techniques with genetic optimization, efficient selection of design variables and search bounds, and a nested optimization structure. A case study illustrating these methods is presented in which a hydraulic rescue spreader is simultaneously optimized for four objectives relating to structural efficiency and kinematic behavior. The solution obtained is shown to be equal or superior to a comparable commercially available device with respect to all four objectives. [ABSTRACT FROM AUTHOR]

The effective distribution of critical relief in post disaster plays a crucial role in post-earthquake rescue operations. The location of distribution centers and vehicle routing in the available transportation network are two of the most challenging issues in emergency logistics. This paper constructs a nonlinear integer open location-routing model for relief distribution problem considering travel time, the total cost, and reliability with split delivery. It proposes the non-dominated sorting genetic algorithm and non-dominated sorting differential evolution algorithm to solve the proposed model. A case study on the Great Sichuan Earthquake in China expounds the application of the proposed models and algorithms in practice. [ABSTRACT FROM AUTHOR]

This paper presents an optimized fuzzy logic control (FLC) applied to four-wheel independent-drive electric vehicles (FWID-EVs). The controller is developed to act as an Electronic Differential (ED), which changes the in-wheel motors torques to correct the vehicle trajectory during some specific maneuvers. The simulation determines the rotation, the lateral and longitudinal displacement of the vehicle, in which the nonlinear Magic Formula is applied to define the tires slip, associated with the load transfer during curves or drive/break situations and with the steering input. According to the literature, several parameters are used to provide stability controls of similar vehicles, for example, the sideslip angle and yaw rate. Therefore, a parameter influence analysis is carried out to find out the most relevant parameters to be monitored in the investigated FWID-EV. Once the relevant input parameters are defined, an optimum ED FLC is developed by means of a genetic algorithm multi-objective optimization with the objective of minimizing the trajectory error of the FWID-EV under a combination of the Sine with Dwell and constant steering standard maneuvers. The results show the best control configuration is a combination of steer and sideslip angles, with a reduced number of rules and less processing time (0.71 s). Besides, the proposed control presents a reduction in the trajectory error, improvement up to 95.1%, emphasizing that the parameter influence analysis allows finding an optimum, more efficient, and time-saving control solution. Therefore, a simple adjustment in the variables inputs can decrease the control process, without overshadowing the dynamic behavior. [Display omitted] • Influence analysis of vehicle stability control input parameters. • Multi-objective optimization procedure for a fuzzy vehicle stability control. • Trajectory correction by means of electronic differential in-wheel EM control. • Best control performance reached with only two input parameters. [ABSTRACT FROM AUTHOR]

The modeling and optimization of a Pulse Tube Refrigerator is a complicated task, due to its complexity of geometry and nature. The aim of the present work is to optimize the dimensions of pulse tube and regenerator for an Inertance-Type Pulse Tube Refrigerator (ITPTR) by using Response Surface Methodology (RSM) and Non-Sorted Genetic Algorithm II (NSGA II). The Box-Behnken design of the response surface methodology is used in an experimental matrix, with four factors and two levels. The diameter and length of the pulse tube and regenerator are chosen as the design variables where the rest of the dimensions and operating conditions of the ITPTR are constant. The required output responses are the cold head temperature (Tcold) and compressor input power (Wcomp). Computational fluid dynamics (CFD) have been used to model and solve the ITPTR. The CFD results agreed well with those of the previously published paper. Also using the results from the 1-D simulation, RSM is conducted to analyse the effect of the independent variables on the responses. To check the accuracy of the model, the analysis of variance (ANOVA) method has been used. Based on the proposed mathematical RSM models a multi-objective optimization study, using the Non-sorted genetic algorithm II (NSGA-II) has been performed to optimize the responses. [ABSTRACT FROM AUTHOR]

The performance of combined cooling, heating and power (CCHP) system is greatly affected by its operating strategy and design. In this paper, a new electric load following (NELF) strategy was developed. It is based on the alternation between absorption cooling and electric cooling according to the building energy requirements, for hybrid chiller based CCHP systems. A comparison of the new proposed strategy with the modified electric load following (MELF) and electric load following (ELF) strategies is performed. A multi-objective optimization approach based on genetic algorithm is carried out to predict the optimal capacity of CCHP systems. Performance criteria like primary energy consumption, annual total cost and carbon dioxide emission were considered as objective functions. The performances of these CCHP systems and operation strategies were examined and compared with the separated production (SP) system for a Mosque complex located in Algiers, Algeria. Results show that hybrid chiller CCHP based NELF strategy is the best choice, which can reduce the primary energy consumption by 34.45 GWh/year, annual total cost by 0.313 million €/year and carbon dioxide emission by 8.37 kton/year. Compared to the other configurations and strategies, the hybrid CCHP based NELF achieves better energetic, economic and environmental performance under the optimized conditions. • A new electric load following strategy for CCHP system is proposed and investigated. • Facilities capacity are predicted by genetic algorithm and decision-making process. • The new strategy achieves better performance compared to conventional strategies. • Annual cost and CO 2 emission reductions of 0.313 M€/y and 8.37 kton/y are obtained. [ABSTRACT FROM AUTHOR]

• Sixty optimization-based calibration approaches for building models are compared. • An automated, genetic multi-objective optimization is developed in MATLAB. • A full-specified case study without uncertainty in model inputs and outputs is used. • The accuracy of the approaches in calibration variables prediction is presented. • CV(RMSE) is the most robust error function under different calibration datasets. Building Energy Model (BEM) calibration is the process of reducing the gap between the simulation outputs and the actual measured data at the same conditions. The literature shows that BEM calibration approaches could lead to a significant error in the model inputs even the calibration has been conducted successfully based on the model outputs (i.e., error functions). This paper compares the performance (i.e., accuracy and robustness) of 60 optimization-based calibration approaches. The approaches have different error functions (individual or combination of NMBE, NME, CV(RMSE), R 2 , C χ2) to be minimized and different outputs (heating demand, cooling demand, and\or indoor temperature for weeks, months, or a year) to be calibrated. The BESTEST600, predefined by ANSI/ASHRAE 140–2001, is selected as a white-box BEM case study for conducting the comparison test. Having the case study inputs and outputs without uncertainty gives a trustworthy comparison between the tested approaches. EnergyPlus is used for conducting the simulation while the Multi-objective optimization algorithm (a variant of NSGA-II) from MATLAB is used to minimize the error function(s) associated to each calibration approach. Among the 60 calibration approaches, eight proved to be the most accurate in predicting all calibration variables with percentage errors lower than 10%. CV(RMSE) was found to be the most robust error function under different calibration datasets. The results also show that the current standard calibration requirements are not proper as stopping criteria for automatic optimization-based calibration. [ABSTRACT FROM AUTHOR]

• Well placement optimization is one of the major challenging factor from optimizations point of view as high dimensional optimization problem. • Stochastic algorithms - such as Particle swarm optimization, and Differential Evolution have been implemented in the well placement optimization. • The review of natures inspired meta heuristic techniques, and their application to maximize the economic factors have been investigated. • The future endeavor, exploiting multiple optimization processes for developing efficient Proxy model is expected. Well placement optimization is one of the major challenging factors in the field development process of oil and gas industry. The objective function of well placement optimization is considered as high dimensional, discontinuous and multi-model. Over the last decade, both gradient-based and gradient-free optimization methods have been implemented to tackle this problem. Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. These optimization techniques are implemented as stand-alone or as hybrid form to maximize the economic factors. In this paper, several nature-inspired metaheuristic optimization techniques and their application to maximize the economic factors are reviewed. Newly developed optimization algorithms are very efficient and favorable when compared to other established optimization algorithms and in all cases, it has been noticed that hybridization of two or more algorithms works better than stand-alone algorithms. Furthermore, none of the single optimization techniques can be established as being universally superior which aligns with no free lunch theorem. For future endeavor, combining optimization methods and exploiting multiple optimization processes for faster convergence and developing efficient proxy model is expected. [ABSTRACT FROM AUTHOR]

• Automatic diagnosis strategy based on bony landmarks. • Two-stage weighted multi-objective optimization based on a genetic algorithm. • Novel bone protrusion evaluation considering bone contact and surfaces gaps. • Patient-specific screw optimization based on bone density information. • Capability of considering all types of common osteotomies: single-cut, opening wedge, closing wedge. Three-dimensional (3D) computer-assisted corrective osteotomy has become the state-of-the-art for surgical treatment of complex bone deformities. Despite available technologies, the automatic generation of clinically acceptable, ready-to-use preoperative planning solutions is currently not possible for such pathologies. Multiple contradicting and mutually dependent objectives have to be considered, as well as clinical and technical constraints, which generally require iterative manual adjustments. This leads to unnecessary surgeon efforts and unbearable clinical costs, hindering also the quality of patient treatment due to the reduced number of solutions that can be investigated in a clinically acceptable timeframe. In this paper, we propose an optimization framework for the generation of ready-to-use preoperative planning solutions in a fully automatic fashion. An automatic diagnostic assessment using patient-specific 3D models is performed for 3D malunion quantification and definition of the optimization parameters' range. Afterward, clinical objectives are translated into the optimization module, and controlled through tailored fitness functions based on a weighted and multi-staged optimization approach. The optimization is based on a genetic algorithm capable of solving multi-objective optimization problems with non-linear constraints. The framework outputs a complete preoperative planning solution including position and orientation of the osteotomy plane, transformation to achieve the bone reduction, and position and orientation of the fixation plate and screws. A qualitative validation was performed on 36 consecutive cases of radius osteotomy where solutions generated by the optimization algorithm (OA) were compared against the gold standard solutions generated by experienced surgeons (Gold Standard; GS). Solutions were blinded and presented to 6 readers (4 surgeons, 2 planning engineers), who voted OA solutions to be better in 55% of the time. The quantitative evaluation was based on different error measurements, showing average improvements with respect to the GS from 20% for the reduction alignment and up to 106% for the position of the fixation screws. Notably, our algorithm was able to generate feasible clinical solutions which were not possible to obtain with the current state-of-the-art method. Image, graphical abstract [ABSTRACT FROM AUTHOR]

• A robust optimization framework is proposed for the dew point evaporative cooler. • Two optimization methods are developed using genetic algorithm. • A 2-D thermodynamic model is coupled with the optimization algorithms. • The optimization results from the two methods are discussed and compared. • The multi-to-single-objective optimization simplifies the optimization complexity. Dew point evaporative cooling is an essential alternative to conventional vapor compression chillers in air conditioning systems to reduce electricity consumption and carbon emission. In this paper, we propose a robust optimization framework of the dew point evaporative cooler towards favorable dew point effectiveness, cooling capacity and Coefficient of Performance (COP). Two optimization algorithms, i.e., multi-to-single-objective and multi-objective optimizations, are developed using genetic algorithm. Concurrently, a 2-D thermodynamic model is established for a counter-flow dew point evaporative cooler and coupled with the optimization algorithms. The model agrees well with the experimental tests on a cooler prototype, and the maximum discrepancy is within ±4.7%. The proposed optimization study is then carried out to investigate the ultimate objective functions and their corresponding decision variables. Key findings that have emerged from this study reveal that the multi-to-single-objective optimization is able to obtain appropriate objective functions according to predefined preference with less complexity and faster response, compared to a multi-objective optimization. The optimal channel length and working ratio are found to be constant in different scenarios, i.e., at 0.50 m and 0.40, respectively, hence they can be eliminated to reduce the number of decision variables. [ABSTRACT FROM AUTHOR]