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The metaheuristic algorithms and their hybridization have been utilized successfully in the past to extract the parameters of photovoltaic (PV) cells and panels. The novelty of the paper consists of proposing the black widow optimization algorithm (BWOA) for the first time to identify the parameters of the two photovoltaic cells RTC France, amorphous silicon (aSi), and two photovoltaic panels PWP201, PVM 752 GaAs. The single-diode model (SDM) and double-diode model (DDM) for analyzing the PVs are considered. The performance of the BWOA is verified using four statistical tests: the root mean square error, which is the primary tool, the mean relative error, the mean bias error, and the coefficient of determination. The research results of this study are as follows: BWOA gave the same results, or very slightly better, for RTC and PWP201 for SDM in comparison with the best algorithms from the specialized literature; for all the other cases, BWOA has substantially better results, especially for PVM 752 GaAs, where the improvements in RMSE are: 16.5%, for PWP201: 6.25%, and for aSi: 5.3%, all for the DDM; the computing time is around 2 s, which is one of the lowest durations. A consistent study is made to optimize the accuracy and computational time in function of the number of iterations and population. [ABSTRACT FROM AUTHOR]

Aged road pavements and insufficient maintenance budgets, along with increasing concerns over the environmental issues related to transportation have introduced additional challenges to highway agencies. Multiobjective optimisation techniques can be used to account for those multiple aspects in the design of maintenance and rehabilitation (M&R) strategies. Contrary to the single-objective optimisation problems where a single solution is optimal, the solution of multiobjective optimisation problems is a set of non-dominated solutions, often referred to as Pareto-optimal set. This set of optimal solutions represents the trade-off between the different and often conflicting objectives, and in many cases is comprised by a vast number of elements. This paper presents the development and application of a fuzzy logic expert system for selecting a single solution from the Pareto set obtained from the multiobjective optimisation of sustainable pavement M&R strategies. It provides decision-makers with an easy and intuitive methodology for the selection of the most preferred solution according to sustainability criteria. The proposed system is applied to a case study from France. Posteriorly, different strategies reflecting the decision-maker's preferences towards economic and environmental objectives are analysed. Conclusions and recommendations for future improvements are derived from this application. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are widely used in environmental applications where the aim is to sense physical phenomena, such as temperature and air pollution. A careful deployment of sensors is necessary in order to get a better knowledge of these physical phenomena while ensuring the minimum deployment cost. In this paper, we focus on using WSN for air pollution mapping and tackle the optimization problem of sensor deployment. Unlike most of the existing deployment approaches that are either generic or assume that sensors have a given detection range, we define an appropriate coverage formulation based on an interpolation formula that is adapted to the characteristics of air pollution sensing. We derive, from this formulation, two deployment models for air pollution mapping using the integer linear programming while ensuring the connectivity of the network and taking into account the sensing error of nodes. We analyze the theoretical complexity of our models and propose the heuristic algorithms based on the linear programming relaxation and binary search. We perform extensive simulations on a dataset of the Lyon city, France, in order to assess the computational complexity of our proposal and evaluate the impact of the deployment requirements on the obtained results. [ABSTRACT FROM AUTHOR]

The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization problem that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this problem due to their potential to approximate the optimal solution for several complicated optimization problems. Despite that, the existing metaheuristic algorithms still suffer from sluggish convergence rates and stagnation in local optima when applied to tackle this problem. Therefore, this study presents a new parameter estimation technique, namely HKOA, based on integrating the recently published Kepler optimization algorithm (KOA) with the ranking-based update and exploitation improvement mechanisms to accurately estimate the unknown parameters of the third-, single-, and double-diode models. The former mechanism aims at promoting the KOA's exploration operator to diminish getting stuck in local optima, while the latter mechanism is used to strengthen its exploitation operator to faster converge to the approximate solution. Both KOA and HKOA are validated using the RTC France solar cell and five PV modules, including Photowatt-PWP201, Ultra 85-P, Ultra 85-P, STP6-120/36, and STM6-40/36, to show their efficiency and stability. In addition, they are extensively compared to several optimization techniques to show their effectiveness. According to the experimental findings, HKOA is a strong alternative method for estimating the unknown parameters of PV models because it can yield substantially different and superior findings for the third-, single-, and double-diode models. [ABSTRACT FROM AUTHOR]

Electrical characteristics of the photovoltaic (PV) solar cells are in general provided by the manufacturer's datasheet; they should not, therefore, be regarded as complete or sufficient to ensure the correct design and installation of a PV system. It is necessary to accurately design the PV system by estimating the intrinsic PV parameters. Attempts to deal with this problem have been mostly realized using several metaheuristic optimization algorithms. However, self-tuning the optimized parameters and avoiding premature convergence are the main challenges of most popular optimization algorithms. This paper proposes an improved optimization algorithm by combining the gradient-based optimizer with the symmetric chaotic sequence generator to estimate the PV parameters of solar cells and modules. The sequence is generated based on a modified form of the Henon map to guarantee the diversity of the initial population, speed up the convergence rate, and enhance global search capability. The proposed algorithm achieves the lowest root mean square error (RMSE) and standard deviation (SD) values for almost all of the PV cells/modules considered in this paper when compared to some state-of-the-art algorithms (RTC France silicon solar cell: RMSE = 9.8248E−04, SD = 2.4484E−14 ; PhotoWatt-PWP 201 PV module: RMSE = 2.4250E−03, SD = 8.9911E−05 ; Poly-solar module type 320W-72P: RMSE 2.5725E−02, SD = 1.0122E−06 ; The Kyocera KC200GT multi-crystalline module: RMSE = 2.3845E−04, SD = 2.0462E−14), confirming its superiority in terms of accuracy, reliability, and excellent convergence ability. The effectiveness of the proposed optimization algorithm is verified by providing the estimated PV parameters for various solar cells and modules data set, under different operating conditions, to facilitate the comparison with other optimization algorithms. • The paper improves the gradient-based optimizer using a symmetric chaos process. • A symmetric chaotic sequence is generated to modify the random parameters of GBO. • The proposed SC-GBO is employed to estimate the PV parameters of different PV models. • The results obtained using SC-GBO are compared with GBO and other recent algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for driving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields. • The Opt-Sim Framework includes the inherent stochasticity of dynamic operations. • The Opt-Sim Framework improves the robustness of the airport capacity management. • Conflicts are reduced by about 57% compared to a non-optimized solution. • The conflict severity for airspace and runway is introduced as a new metric. • The framework developed is generic and can be extended to other fields. [ABSTRACT FROM AUTHOR]

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient. [ABSTRACT FROM AUTHOR]

The development of reliable simulators that finely imitate the behavior of PV devices is vitally important for the design and optimization of efficient and stable photovoltaic systems. In this work, an improved variant of the African Vultures Optimization Algorithm named IAVOA is designed to serve as a powerful tool for extracting the unknown parameters of photovoltaic models. The introduced scheme incorporates a twofold strategy in such a way that allows a portion of the search agents to conduct a global search while the remaining portion performs a local search. The embedded mechanism is based on two equations added to the standard version, and by which the exploration and exploitation capabilities of the algorithm have significantly been fostered. To testify the performance of the IAVOA, a comparative study based on the Root Mean Square Error (RMSE), was conducted on six distinct benchmark PV models, and the obtained results were, in most cases, remarkably superior to the ones achieved by its competitors. The algorithm was able to produce values for the ideality factors that have not been previously found by any existing work to the best of our knowledge. In turn, the Double Diode and Triple Diode models' accuracies were notably improved with RMSE scores of 6.9096 × 10 − 4 and 7.4011 × 10 − 4 respectively for the RTC France cell, and 1.4251 × 10 − 2 for the STP6-120/36 module, outperforming the existing techniques. In light of that, it can be reliably presumed that the IAVOA is indeed a promising algorithm for the electrical characterization of PV devices. [ABSTRACT FROM AUTHOR]

• A new container drayage problem with availability constraints is investigated. • A mathematical mixed-integer linear programming model is built. • A hybrid VNS approach based on VND and Tabu Search is developed. • The approach is embedded into a simulation to achieve more realistic results. • Extensive experiments are conducted to validate the proposed approach. The paper discusses a containers' drayage by trucks, which is motivated by a real-life application arising in the intermodal freight transportation. This problem considers not only classical assumptions of the intermodal transportation, but also introduces the availability of containers as a new concept. For this purpose, we first describe the industrial context and provide the mathematical optimization model which combines a set of known and new constraints. We then propose a solution approach based on variable neighborhood search algorithm combined with pairing and insertion heuristics, as well as with variable neighborhood descent and tabu search methods. Optimization has then been embedded into a simulation model which designs the rail/road terminal. To be more specific, this optimization model can be used to evaluate several distribution policies, which results in a set of routing decisions and can adopt the best policy in a distribution network with competing rail and road transport services. Finally, we test the proposed model on real and generated instances characterizing several scenarios to highlight the performance and efficiency of our approach. [ABSTRACT FROM AUTHOR]

• We address shortcomings of RMSE calculation of 5-parameter single diode PV models. • We propose an exact solution for RMSE of 5-parameter single diode PV models. • The proposed RMSE expression is based on Lambert W function. • Precision of calculation to solve the Lambert W function is addressed. • Results and applications show why the proposed RMSE expression must be adopted. In the literature, one can find a lot of methods and techniques employed to estimate single diode solar photovoltaic (PV) cell parameters. The efficiency of these methods is usually tested by calculating the Root Mean Square Error (RMSE) between the measured and estimated values of the solar PV cell output current. In this work, first, the values of RMSE calculated using 69 different methods published in many journal papers for the well-known RTC France solar PV cell are presented and discussed. Second, a novel exact analytical solution for RMSE calculation based on the Lambert W function is proposed. The results obtained show that the RMSE values were not calculated correctly in most of the methods presented in the literature since the exact expression of the calculated cell output current was not used. Third, the precision of calculation of the methods used for analytical solving of Lambert W equation is presented and discussed. Fourth, the applicability of the proposed solution methodology in accordance with current-voltage characteristics measured in the laboratory for solar modules of Clean Energy Trainer Setup is checked. Identification of its unknown parameters is presented using three optimization techniques. Further, the proposed solution methodology is proven for Solarex MSX–60 PV module, and the most promising 5-parameter single diode parameters are estimated based on minimization of the precise RMSE values calculated. Finally, this work aimed to develop a good base for proper investigation and implementation of optimization algorithms to solve the parameter estimation problem of 5-parameter single diode PV equivalent circuits. [ABSTRACT FROM AUTHOR]

Efficient and accurate estimations of unidentified parameters of photovoltaic (PV) models are essential to their simulation. This study suggests two new variants of the whale optimization algorithm (WOA) for identifying the nine parameters of the three-diode PV model. The first variant abbreviated as RWOA is based on integrating the WOA with ranking methods under a novel updating scheme to utilize each whale within the population as much as possible during the optimization process. The second variant, namely HWOA, has been based on employing a novel cyclic exploration-exploitation operator with the RWOA to promote its local and global search for averting stagnation into local minima and accelerating the convergence speed in the right direction of the near-optimal solution. Experimentally, RWOA and HWOA are validated on a solar cell (RTC France) and two PV modules (Photowatt-PWP201 and Kyocera KC200GT). Further, these proposed variants are compared with five well-known parameter extraction models in order to demonstrate their notable advantages over the other existing competing algorithms for minimizing the root mean squared error (RMSE) between experimentally measured data and estimated one. The experimental findings show that RWOA is superior in some observed cases and superior in the other cases in terms of final accuracy and convergence speed; yet, HWOA is superior in all cases. [ABSTRACT FROM AUTHOR]