Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems – Part 2: Constrained optimization

This paper is the second one of the two papers entitled “Weighted Superposition Attraction (WSA) Algorithm”, which is about the performance evaluation of the WSA algorithm in solving the constrained global optimization problems. For this purpose, the well-known mechanical design optimization problems, design of a tension/compression coil spring, design of a pressure vessel, design of a welded beam and design of a speed reducer, are selected as test problems. Since all these problems were formulated as constrained global optimization problems, WSA algorithm requires a constraint handling method for tackling them. For this purpose we have selected 6 formerly developed constraint handling methods for adapting into WSA algorithm and analyze the effect of the used constraint handling method on the performance of the WSA algorithm. In other words, we have the aim of producing concluding remarks over the performance and robustness of the WSA algorithm through a set of computational study in solving the constrained global optimization problems. Computational study indicates the robustness and the effectiveness of the WSA in terms of obtained results, reached level of convergence and the capability of coping with the problems of premature convergence, trapping in a local optima and stagnation.     

Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.     

A practical assessment of risk-averse approaches in production lot-sizing problems

ABSTRACT

This paper presents an empirical assessment of four state-of-the-art risk-averse approaches to deal with the capacitated lot-sizing problem under stochastic demand. We analyse two mean-risk models based on the semideviation and on the conditional value-at-risk risk measures, and alternate first and second-order stochastic dominance approaches. The extensive computational experiments based on different instances characteristics and on a case-study suggest that CVaR exhibits a good trade-off between risk and performance, followed by the semideviation and first-order stochastic dominance approach. For all approaches, enforcing risk-aversion helps to reduce the cost-standard deviation substantially, which is usually accomplished via increasing production rates. Overall, we can say that very risk-averse decision-makers would be willing to pay an increased price to have a much less risky solution given by CVaR. In less risk-averse settings, though, semideviation and first-order stochastic dominance can be appealing alternatives to provide significantly more stable production planning costs with a marginal increase of the expected costs.     

Framework to manage multiple goals in community-based energy sharing network in smart grid

Smart grid has opened up a new role of “prosumer” in an energy value network, transforming many conventional energy consumers into prosumers, who not only generate green energy but also share the surplus with utilities and other consumers. The concept of a goal-oriented prosumer community group (PCG) has emerged recently as an effective way to fulfill sustainable energy exchange. Such community-based energy sharing networks comprise multiple irreconcilable objectives such as demand constraints, cost constraints, and income maximization. In many cases, one goal may be achievable only at the expense of other goals. This necessitates the development of an effective framework to manage the multiple goals and reduce the gap with their achievement levels. Therefore, in this research paper, an effective framework is developed to negotiate among the multiple goals and thus to define optimal mutual goals for each PCG in a more sustainable manner using multiple-criteria goal programming techniques. Simulation results are presented to illustrate how the methods work in practical situations, where each of the objective measure is given a target value and the unwanted deviations from this set are minimized in an achievement function.     

Global optimization for power dispatch problems based on theory of moments

Non-convex of an optimal power dispatch problem makes it difficult to guarantee the global optimum. This paper presents a convex relaxation approach, called the Moment Semidefinite Programming (MSDP) method, to facilitate the search for deterministic global optimal solutions. The method employs a sequence of moments, which can linearize polynomial functions and construct positive semidefinite moment matrices, to form an SDP convex relaxation for power dispatch problems. In particular, the rank of the moment matrix is used as a sufficient condition to ensure the global optimality. The same condition can also be leveraged to estimate the number of global optimal solution(s). This method is effectively applied to {0,1}-economic dispatch (ED) problems and optimal power flow (OPF) problems. Simulation results showed that the MSDP method is capable of solving {0,1}-ED problems with integer values directly, and is able to identify if more than one global optimal solutions exist. In additional, the method can obtain rank-1 moment matrices for OPF’s counterexamples of existing SDP method, this ensures the global solution and overcomes the problem that existing SDP method cannot meet the rank-1 condition sometimes.     

Kariya (Hildegardia barteri) seed oil extraction: comparative evaluation of solvents,modeling, and optimization techniques

In this work, the effects of solid/solvent ratio (0.10–0.25?g/ml), extraction time (3–8?h), and solvent type (n-hexane, ethyl acetate, and acetone) together with their shared interactions on Kariya seed oil (KSO) yield were investigated. The oil extraction process was modeled via response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) while the optimization of the three input variables essential to the oil extraction process was carried out by genetic algorithm (GA) and RSM methods. The low mean relative percent deviation (MRPD) of 0.94–4.69% and high coefficient of determination (R²) > 0.98 for the models developed demonstrate that they describe the solvent extraction process with high accuracy in this order: ANFIS, ANN, and RSM. The best operating condition (solid/solvent ratio of 0.1?g/ml, extraction time of 8?h, and acetone as solvent of extraction) that gave the highest KSO yield (32.52?wt.%) was obtained using GA-ANFIS and GA-ANN. Solvent extraction efficiency evaluation showed that ethyl acetate, n-hexane, and acetone gave maximum experimental oil yields of 19.20?±?0.28, 25.11?±?0.01, and 32.33?±?0.04?wt.%, respectively. Properties of the KSO varied based on the type of solvent used. The results of this work showed that KSO could function as raw material in both food and chemical industries.     

Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation

The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the morphology profile and the chemical composition. Here we fit such a model to the different patterns present in a range of electrodeposited and electrochemically modified alloys using PDE constrained optimization. Experiments with simulated data show how the parameter space of the model can be divided into zones corresponding to the different physical patterns by examining the structure of an appropriate cost function. We then use real data to demonstrate how numerical optimization of the cost function can allow the model to fit the rich variety of patterns arising in experiments. The computational technique developed provides a potential tool for tuning experimental parameters to produce desired patterns.     

Application of the Microsoft Excel Solver tool in the solution of optimization problems of heat exchanger network systems

Optimization of heat exchanger networks (HEN_S) is a field of interest in Chemical Engineering. The aim is to use the residual heat of process streams to raise the temperature of other process streams which are cold, thus minimizing the use of hot and cold utilities. The usual way to deal with this task is to pose a linear programming problem according to a transshipment model in order to minimize either the general services consumptions or the number of hot and cold stream matchings required. This optimization is subjected to several restrictions given by the energy balances corresponding to each heat exchange.The high number of variables and equations obliges the use of specific software to solve these problems, e.g. GAMS (General Algebraic Modelling System). However, this software is not intuitive and requires a long time of training and a certain level of practice for the students to adequately use it.This work describes the use of Microsoft Excel-Solver for the sequential solving of HEN_S optimization problems according to the transshipment model. Solver is a much simpler, intuitive and easily available tool for the students who, besides, usually learn how to use it in the first years of their degree, in Informatics or similar subjects. Thus, it is not necessary to spend extra time in the teaching of the software, time that may be devoted to other contents.This paper presents an example of HEN_S problem proposed in the subject Simulation and Optimization of Chemical Processes corresponding to the Joint Master in Chemical Engineering of the Rey Juan Carlos University and the Autónoma University of Madrid and solved by the students using Excel-Solver.     

The generalized discrete ‐centroid problem

The ‐centroid problem or leader–follower problem is generalized considering different customer choice rules where a customer may use facilities belonging to different firms, if the difference in travel distance (or time) is small enough. Assuming essential goods, some particular customer choice rules are analyzed. Linear programming formulations for the generalized ‐medianoid and ‐centroid problems are presented and an exact solution approach is applied. Some computational examples are included.