Energy-efficient routing technique for reliable data transmission under the background of big data for disaster region

Big data analysis and cloud computing are gaining much interest in various applications including disaster management. One of the major difficulties in the process of exchanging environmental data in the disaster affected areas has been considered as one of the emerging areas of research. This research focuses on maintaining the environmental data information management of the disaster affected areas, where the intermediate node has been used to transmit the information during transmission and an optimized routing has been used to create efficient data transmission, such as temperature, pressure, humidity, and the level of pollution within the network. The intermediate node may also be hacked during data processing. In this article, the efficient big data-based clustering technique has been proposed. In this research, the information is grouped into a cluster in every comparable node and the energy consumption has been efficiently managed with the hybrid metaheuristic optimization-based effective routing technique. The system excellence has been evaluated using the energy utilization factor, packet delivery ratio, and attack-free routing effectiveness metrics to handle environmental information on disaster affected areas.     

Evaluation of different initial solution algorithms to be used in the heuristics optimization to solve the energy resource scheduling in smart grids

Over the last years, an increasing number of distributed resources have been connected to the power system due to the ambitious environmental targets, which resulted into a more complex operation of the power system. In the future, an even larger number of resources is expected to be coupled which will turn the day-ahead optimal resource scheduling problem into an even more difficult optimization problem. Under these circumstances, metaheuristics can be used to address this optimization problem. An adequate algorithm for generating a good initial solution can improve the metaheuristic’s performance of finding a final solution near to the optimal than using a random initial solution. This paper proposes two initial solution algorithms to be used by a metaheuristic technique (simulated annealing). These algorithms are tested and evaluated with other published algorithms that obtain initial solution. The proposed algorithms have been developed as modules to be more flexible their use by other metaheuristics than just simulated annealing. The simulated annealing with different initial solution algorithms has been tested in a 37-bus distribution network with distributed resources, especially electric vehicles. The proposed algorithms proved to present results very close to the optimal with a small difference between 0.1%. A deterministic technique is used as comparison and it took around 26 h to obtain the optimal one. On the other hand, the simulated annealing was able of obtaining results around 1 min.     

Optimal Power Flow Using Differential Search Algorithm

—In this article, a new nature-inspired metaheuristic technique called the differential search algorithm is proposed to solve the optimal power flow problem. The proposed differential search algorithm has been developed and tested under normal and contingency power system conditions. To show the effectiveness of the proposed method, it has been demonstrated on the standard IEEE 30-bus and IEEE 118-bus test systems with different objectives that reflect performance indices of the power system. Obtained results using the proposed technique indicate that the proposed differential search algorithm provides an effective, a robust, and a high-quality solution for the optimal power flow problem. The comparisons of the proposed differential search algorithm results with those reported in the literature reveal the potential and superiority of the proposed algorithm in terms of the optimal solution quality and robustness.     

Tabu Search Heuristic for Point-Feature Cartographic Label Placement

The generation of better label placement configurations in maps is a problem that comes up in automated cartographic production. The objective of a good label placement is to display the geographic position of the features with their corresponding label in a clear and harmonious fashion, following accepted cartographic conventions. In this work, we have approached this problem from a combinatorial optimization point of view, and our research consisted of the evaluation of the tabu search (TS) heuristic applied to cartographic label placement. When compared, in real and random test cases, with techniques such as simulated annealing and genetic algorithm (GA), TS has proven to be an efficient choice, with the best performance in quality. We concluded that TS is a recommended method to solve cartographic label placement problem of point features, due to its simplicity, practicality, efficiency and good performance along with its ability to generate quality solutions in acceptable computational time.     

A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming

The conventional unconstrained binary quadratic programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the single-objective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimized simultaneously. We propose a hybrid metaheuristic which combines an elitist evolutionary multiobjective optimization algorithm and a state-of-the-art single-objective tabu search procedure by using an achievement scalarizing function. Finally, we define a formal model to generate mUBQP instances and validate the performance of the proposed approach in obtaining competitive results on large-size mUBQP instances with two and three objectives.     

Cooperative parallel adaptive neighbourhood search for the disjunctively constrained knapsack problem

This article investigates the use of parallel computing for solving the disjunctively constrained knapsack problem. The proposed parallel computing model can be viewed as a cooperative algorithm based on a multi-neighbourhood search. The cooperation system is composed of a team manager and a crowd of team members. The team members aim at applying their own search strategies to explore the solution space. The team manager collects the solutions from the members and shares the best one with them. The performance of the proposed method is evaluated on a group of benchmark data sets. The results obtained are compared to those reached by the best methods from the literature. The results show that the proposed method is able to provide the best solutions in most cases. In order to highlight the robustness of the proposed parallel computing model, a new set of large-scale instances is introduced. Encouraging results have been obtained.     

System Reliability Optimization Using Gray Wolf Optimizer Algorithm

For the past two decades, nature‐inspired optimization algorithms have gained enormous popularity among the researchers. On the other hand, complex system reliability optimization problems, which are nonlinear programming problems in nature, are proved to be non‐deterministic polynomial‐time hard (NP‐hard) from a computational point of view. In this work, few complex reliability optimization problems are solved by using a very recent nature‐inspired metaheuristic called gray wolf optimizer (GWO) algorithm. GWO mimics the chasing, hunting, and the hierarchal behavior of gray wolves. The results obtained by GWO are compared with those of some recent and popular metaheuristic such as the cuckoo search algorithm, particle swarm optimization, ant colony optimization, and simulated annealing. This comparative study shows that the results obtained by GWO are either superior or competitive to the results that have been obtained by these well‐known metaheuristic mentioned earlier. Copyright © 2016 John Wiley & Sons, Ltd.     

混合模因算法在求解带装箱约束的车辆路径问题中的应用

带有回程取货约束的车辆路径问题(Vehicle Routing Problem with Backhauls,VRPB)和二维装箱问题(two-dimensional Bin Packing Problem,2L-BPP)是两个经典的组合优化问题,在融合两者的基础上,本文提出了一种新的组合最优化问题,即2L-VRPB.在该问题中,车队的最优路径规划和货物的最优装载设计需要同时进行考虑,该问题的优化目标是在满足所有客户的送货和取货需求的前提下,为车队中的车辆制定尽可能最优的行驶路线和货物装载方案,使得车队的总的服务成本最低.该问题在实际生活中有着广泛的应用场景,例如在设备维修和零售行业的货物运输中可经常遇到此类情形,但是文献中关于此类问题的研究论文仍然较少.为了求解2L-VRPB问题,我们提出了一种具有自适应性机制的混合模因算法(HMA),该算法采用改进的模因算法(IMA)来规划最优路径,并通过增强的组合装箱算法(MultiPack)来设计货物的最优装载方案.在实验环节,通过在VRPB问题的Goetschalckx&Jacobs-Blecha测试算例和2L-VRPB问题的Gendreau测试算例上设计对比实验,我们验证了混合模因算法在求解VRPB和2L-VRPB问题时的鲁棒性和有效性.     

Multiobjective fuzzy mathematical model for a financially constrained closed-loop supply chain with labor employment

This paper addresses the multiobjective, multiproducts and multiperiod closed-loop supply chain network design with uncertain parameters, whose aim is to incorporate the financial flow as the cash flow and debts' constraints and labor employment under fuzzy uncertainty. The objectives of the proposed mathematical model are to maximize the increase in cash flow, maximize the total created jobs in the supply chain, and maximize the reliability of consumed raw materials. To encounter the fuzzy uncertainty in this model, a possibilistic programming approach is used. To solve large-sized problems, the multiobjective simulated annealing algorithm, multiobjective gray wolf optimization, and multiobjective invasive weed optimization are proposed and developed. The numerical results demonstrate that these algorithms solve the problems within about 1% of the required solving time for the augmented ε-constraint and have similar performance and even better in some cases. The multiobjective simulated annealing algorithm with a weak performance takes less time than the other two algorithms. The multiobjective gray wolf optimization and multiobjective invasive weed optimization algorithms are superior based on the multiobjective performance indices.     

Cohort intelligence algorithm for discrete and mixed variable engineering problems

Abstract

In this study, the performance of an emerging socio inspired metaheuristic optimization technique referred to as Cohort Intelligence (CI) algorithm is evaluated on discrete and mixed variable nonlinear constrained optimization problems. The investigated problems are mainly adopted from discrete structural optimization and mixed variable mechanical engineering design domains. For handling the discrete solution variables, a round off integer sampling approach is proposed. Furthermore, in order to deal with the nonlinear constraints, a penalty function method is incorporated. The obtained results are promising and computationally more efficient when compared to the other existing optimization techniques including a Multi Random Start Local Search algorithm. The associated advantages and disadvantages of CI algorithm are also discussed evaluating the effect of its two parameters namely the number of candidates, and sampling space reduction factor.