Explore the vast range of titles available.

This paper presents an algorithm for the optimization of the design of reinforced concrete (RC) road bridges, taking into account the current conditions in Cuba. The use of RC is chosen over the common solutions due to the current high economic cost of prestressing steel. The optimization problem is formulated to find the minimum direct cost, defining constraints based on the AASTHO-LRFD 2014 and NC-207:2019 standards. The algorithm is created to properly obtain practical solutions from the engineering point of view, involving the use of discrete variables. The optimization method used is Biogeography-Based Optimization. A sensitivity analysis of the parameters of this method is performed. The results indicate that the typical projects currently used can be considerably improved to enhance their economic indexes. Based on these results, some specific design recommendations are given. In addition, future research lines are suggested based on the deficiencies of the proposed methodology.

In recent years, demand side management programs are in the spotlight due to the evolution of the smart grid and consumer-centric policies. Demand side management program contains many objectives one of the prime objective is to manage energy demand by certain change in consumer demand. This can be achieved by various methods such as financial discount and change in behavior through imparting education to support the stressed conditions of the grid. This paper demonstrates demand side management strategies based upon strategic conservation, peak clipping and load shifting techniques for future smart grids. The grid contains large number of controllable devices. The day before strategic conservation, peak clipping and load shifting techniques discussed in this paper are mathematically derived for minimization problem. A heuristic-based Whale optimization algorithm (WOA) was developed for solving this problem of minimization. Simulations are conducted on a test smart grid that contains a variation in loads in two service areas, one with residential consumers, and another with commercial consumers. WOA proves its efficacy by comparing the results with spider monkey optimization and biogeography based optimization. The simulation results show that proposed demand side management strategies achieve substantial savings, while reducing the peak load demand of the smart grid.

The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine, while posing new challenges. Cognitive radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of cognitive radio networks into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary algorithms are suitable techniques to solve the above-mentioned problem. In this paper, we design a CR engine for wireless multicarrier transceivers using real-coded biogeography-based optimization (RCBBO). The CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. RCBBO uses a mutation operator in order to improve the diversity of the population and enhance the exploration ability of the original BBO algorithm. The simulation results show that the RCBBO driven CR engine can obtain better results than the original BBO and outperform results from the literature. Moreover, RCBBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.

This paper explores biogeography-based learning particle swarm optimization (BLPSO). Specifically, based on migration of biogeography-based optimization (BBO), a new biogeography-based learning strategy is proposed for particle swarm optimization (PSO), whereby each particle updates itself by using the combination of its own personal best position and personal best positions of all other particles through the BBO migration. The proposed BLPSO is thoroughly evaluated on 30 benchmark functions from CEC 2014. The results are very promising, as BLPSO outperforms five well-established PSO variants and several other representative evolutionary algorithms.

This paper presents an effective biogeography-based optimization (BBO) for optimal location and sizing of solar photovoltaic distributed generation (PVDG) units to reduce power losses while maintaining voltage profile and voltage harmonic distortion at the limits. This applied algorithm was motivated by biogeography, that the study of the distribution of biological species through time and space. This technique is able to expand the searching space and retain good solution group at each generation. Therefore, the applied method can significantly improve performance. The effectiveness of the applied algorithm is validated by testing it on IEEE 33-bus and IEEE 69-bus radial distribution systems. The obtained results are compared with the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the artificial bee colony algorithm (ABC). As a result, the applied algorithm offers better solution quality and accuracy with faster convergence.

Full-face tunnel boring machine (TBM) is a modern and efficient tunnel construction equipment. A reliable and accurate TBM performance (like penetration rate, PR) prediction can reduce the cost and help to select the appropriate construction method. Therefore, this study introduces a new hybrid intelligence technique, i.e., grey wolf optimizer-feature weighted-multiple kernel-support vector regression (GWO-FW-MKL-SVR) to predict TBM PR. For this purpose, a tunnel in China was selected as a case study and the most important parameters on TBM performance, i.e., chamber earth pressure, total thrust, cutterhead torque, cutterhead speed, cohesion, internal friction angle, compression modulus, the ratio of boulder, uniaxial compressive strength and rock quality designation, were measured and considered as model inputs. To show the capability of the GWO-FW-MKL-SVR model, three models including biogeography-based optimization (BBO)-FW-MKL-SVR, MKL-SVR, and SVR were also proposed to predict the TBM PR. To select the best predictive models, some performance indices, i.e., coefficient of determination (R2), root mean square error (RMSE) and variance accounted for (VAF) were considered and calculated. The obtained results showed that the GWO-FW-MKL-SVR model receives the highest accuracy in predicting the TBM PR for both train and test stages. R2 values of 0.946 and 0.894, for train and test stages of the GWO-FW-MKL-SVR model, respectively, confirmed that this new hybrid model is considered as a powerful, applicable and simple technique in predicting the TBM PR. By performing feature weight analysis, it was found that the effects of the uniaxial compressive strength, rock quality designation and cutterhead speed features were higher than the other input parameters on the TBM PR.

Summary In this paper, a biogeography‐based optimization is proposed for fault section estimation of distribution systems. This method mimics the geographical distribution of biological species, where the formulation, migration, and extinction of species are all included in the algorithm development. The method comes with relatively few computation steps and owns a larger probability of reaching the quality solution when compared with other published techniques. By establishing the mathematical models for the states of protective devices and fault sections, this biogeography‐based optimization is applied to estimate the fault section on two example systems and three real systems in order to validate the effectiveness of this proposed approach. From simulation results gained from the proposed methods, they help support the feasibility of the method for the fault diagnosis applications. Copyright © 2013 John Wiley & Sons, Ltd.

This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

With the increasing complexity of optimization problems in the real world, more and more intelligent algorithms are used to solve these problems. Elephant herding optimization (EHO), a recently proposed metaheuristic algorithm, is based on the nomadic habits of elephants on the grassland. The herd is divided into multiple clans, each individual drawing closer to the patriarchs (clan updating operator), and the adult males are separated during puberty (separating operator). Biogeography-based optimization (BBO) is inspired by the principles of biogeography, and finally achieves an equilibrium state by species migration and drifting between geographical regions. To solve the numerical optimization problems, this paper proposes an improved elephant herding optimization using dynamic topology and biogeography-based optimization based on learning, named biogeography-based learning elephant herding optimization (BLEHO). In BLEHO, we change the topological structure of the population by dynamically changing the number of clans of the elephants. For the updating of each individual, we use the update of the operator based on biogeography-based learning or the operator based on EHO. In the separating phase, we set the separation probability according to the number of clans, and adopt a new separation operator to carry out the separation operation. Finally, through elitism strategy, a certain number of individuals are preserved directly to the next generation without being processed, thus ensuring a better evolutionary process for the population. To verify the performance of BLEHO, we used the benchmarks provided by IEEE CEC 2014 for the test. The experimental results were compared with some classical algorithms (ABC, ACO, BBO, DE, EHO, GA, and PSO) and the most advanced algorithms (BBKH, BHCS, CCS, HHO, PPSO, SCA, and VNBA) and analyzed by Friedman rank test. Finally, we also applied BLEHO to the simple traveling salesman problem (TSP). The results show that BLEHO has better performance than other methods.

PurposeFor prefabricated building construction, improper handling of the production scheduling for prefabricated components is one of the main reasons that affect project performance, which causes overspending, schedule overdue and quality issues. Prior research on prefabricated components production schedule has shown that optimizing the flow shop scheduling problem (FSSP) is the basis for solving this issue. However, some key resources and the behavior of the participants in the context of actual prefabricated components production are not considered comprehensively.Design/methodology/approachThis paper characterizes the production scheduling of the prefabricated components problem into a permutation flow shop scheduling problem (PFSSP) with multi-optimization objectives, and limitation on mold and buffers size. The lean construction principles of value-based management (VBM) and just-in-time (JIT) are incorporated into the production process of precast components. Furthermore, this paper applies biogeography-based optimization (BBO) to the production scheduling problem of prefabricated components combined with some improvement measures.FindingsThis paper focuses on two specific scenarios: production planning and production rescheduling. In the production planning stage, based on the production factor, this study establishes a multi-constrained and multi-objective prefabricated component production scheduling mathematical model and uses the improved BBO for prefabricated component production scheduling. In the production rescheduling stage, the proposed model allows real-time production plan adjustments based on uncertain events. An actual case has been used to verify the effectiveness of the proposed model and the improved BBO.Research limitations/implicationsWith respect to limitations, only linear weighted transformations are used for objective optimization. In regards to research implications, this paper considers the production of prefabricated components in an environment where all parties in the supply chain of prefabricated components participate to solve the production scheduling problem. In addition, this paper creatively applies the improved BBO to the production scheduling problem of prefabricated components. Compared to other algorithms, the results show that the improved BBO show optimized result.Practical implicationsThe proposed approach helps prefabricated component manufacturers consider complex requirements which could be used to formulate a more scientific and reasonable production plan. The proposed plan could ensure the construction project schedule and balance the reasonable requirements of all parties. In addition, improving the ability of prefabricated component production enterprises to deal with uncertain events. According to actual production conditions (such as the occupation of mold resources and storage resources of completed components), prefabricated component manufacturers could adjust production plans to reduce the cost and improve the efficiency of the whole prefabricated construction project.Originality/valueThe value of this article is to provide details of the procedures and resource constraints from the perspective of the precast components supply chain, which is closer to the actual production process of prefabricated components. In addition, developing the production scheduling for lean production will be in line with the concept of sustainable development. The proposed lean production scheduling could establish relationships between prefabricated component factory manufacturers, transportation companies, on-site contractors and production workers to reduce the adverse effects of emergencies on the prefabricated component production process, and promote the smooth and efficient operation of construction projects.