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161.
Muhammad Irfan Ch. Anwar Ul Hassan Faisal Althobiani Nasir Ayub Raja Jalees Ul Hussen Khan Emad Ismat Ghandourah Majid A. Almas Saleh Mohammed Ghonaim V. R. Shamji Saifur Rahman 《计算机系统科学与工程》2023,46(2):1723-1740
The smart grid has enabled users to control their home energy more effectively and efficiently. A home energy management system (HEM) is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy. Here, we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm (GWA) and Harmony Search Algorithms (HSA). Moreover, a fusion initiated on HSA and GWA operators is used to optimize energy intake. Furthermore, many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge. Hybridization has proven beneficial in achieving numerous objectives simultaneously, decreasing the peak-to-average ratio and power prices. Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method, Harmony GWA (HGWA). The simulations are for a multi-family housing complex outfitted with various cool gadgets. The simulation results indicate that GWA functions better regarding cost savings than HSA. In reputes of PAR, HSA is significantly more effective than GWA. The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR, as opposed to 503.4 in GWA, 398.10 in HSA and 640.3 in HGWA. The suggested approach performed better than HSA and GWA in PAR reduction. For single-family homes in HGWA, GWA and HSA, the reduction in PAR is 45.79%, 21.92% and 20.54%, respectively. The hybrid approach, however, performs better than the currently used nature-inspired techniques in terms of Cost and PAR. 相似文献
162.
Reem Alkanhel Doaa Sami Khafaga El-Sayed M. El-kenawy Abdelaziz A. Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B. M. El-den 《计算机、材料和连续体（英文）》2023,74(2):2695-2709
The Internet of Things (IoT) is a modern approach that enables connection with a wide variety of devices remotely. Due to the resource constraints and open nature of IoT nodes, the routing protocol for low power and lossy (RPL) networks may be vulnerable to several routing attacks. That’s why a network intrusion detection system (NIDS) is needed to guard against routing assaults on RPL-based IoT networks. The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks. Therefore, we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique (LSH-SMOTE). The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms. To prove the effectiveness of the proposed approach, a set of experiments were conducted to evaluate the performance of NIDS for three cases, namely, detection without dataset balancing, detection with SMOTE balancing, and detection with the proposed optimized LSH-SOMTE balancing. Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy. In addition, a statistical analysis is performed to study the significance and stability of the proposed approach. The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset. Based on the proposed approach, the achieved accuracy is (98.1%), sensitivity is (97.8%), and specificity is (98.8%). 相似文献
163.
This paper describes an optimization model to be used by System Operators in order to validate the economic schedules obtained by Market Operators together with the injections from Bilateral Contracts. These studies will be performed off-line in the day before operation and the developed model is based on adjustment bids submitted by generators and loads and it is used by System Operators if that is necessary to enforce technical or security constraints. This model corresponds to an enhancement of an approach described in a previous paper and it now includes discrete components as transformer taps and reactor and capacitor banks. The resulting mixed integer formulation is solved using Simulated Annealing, a well known metaheuristic specially suited for combinatorial problems. Once the Simulated Annealing converges and the values of the discrete variables are fixed, the resulting non-linear continuous problem is solved using Sequential Linear Programming to get the final solution. The developed model corresponds to an AC version, it includes constraints related with the capability diagram of synchronous generators and variables allowing the computation of the active power required to balance active losses. Finally, the paper includes a Case Study based on the IEEE 118 bus system to illustrate the results that it is possible to obtain and their interest. 相似文献
164.
The post-enrolment course timetabling (PE-CTT) is one of the most studied timetabling problems, for which many instances and results are available. In this work we design a metaheuristic approach based on simulated annealing to solve the PE-CTT. We consider all the different variants of the problem that have been proposed in the literature and we perform a comprehensive experimental analysis on all the available public instances. The outcome is that our solver, properly engineered and tuned, performs very well on all cases, providing the new best known results on many instances and state-of-the-art values for the others. 相似文献
165.
《Materials and Manufacturing Processes》2012,27(2):119-129
This article provides a short introduction to the evolutionary multiobjective optimization field. The first part of the article discusses the most representative multiobjective evolutionary algorithms that have been developed, from a historical perspective. In the second part of the article, some representative applications within materials science and engineering are reviewed. In the final part of the article, some potential areas for future research in this area are briefly described. 相似文献
166.
One typical golf tournament format is termed a 'Scramble,' comprised of four-person teams. The participants are rank-ordered into four equally sized 'flights' based on integer-valued handicaps determined by skill level. One participant from each flight is selected to make up a team. Of interest is the assignment of teams in an 'equitable' fashion, where equitable is defined as minimizing the difference between the largest and smallest sum of the handicaps. For a typical tournament of 36 teams there are over 10 124 unique assignments. Since in general there are duplicate handicap values, the number of 'equivalent' assignments is reduced (but still very large). Various heuristics are explored for efficiently identifying an optimal or near optimal solution. These include descent heuristics, simulated annealing, tabu search, and genetic algorithms. Genetic algorithms outperform other heuristics by taking advantage of the problem structure. 相似文献
167.
In this paper, we address two metaheuristic approaches, a Variable Neighborhood Search (VNS) and an Electromagnetism-like metaheuristic (EM), on an NP-hard optimization problem: Multi-dimensional Two-way Number Partitioning Problem (MDTWNPP). MDTWNPP is a generalization of a Two-way Number Partitioning Problem (TWNPP), where a set of vectors is partitioned rather than a set of numbers. The simple k-swap neighborhoods allow an effective shaking procedure in the VNS search. The attraction–repulsion mechanism of EM is extended with a scaling procedure, which additionally moves EM points closer to local optima. Both VNS and EM use the same local search procedure based on 1-swap improvements. Computational results were obtained on 210 standard instances. Direct comparison with results from the literature confirm the significance of applying these methods to MDTWNPP. 相似文献
168.
In this work we have developed a Variable Neighborhood Search (VNS) method in order to solve the MaxMinMin p-dispersion problem, which adds a new type of plateau search mechanism to the classical VNS metaheuristic framework. Besides, several other contributions have been made to the basic VNS heuristic in terms of the ascent and perturbation functions. To the best of our knowledge this is the first application of the VNS to the MaxMinMin problem and our approach, compared to previous methods, finds or improves the results for all of the large-sized benchmarks with low computational efforts. Finding most of the proven optimal solutions in a fraction of a second, the robustness and quality of the solutions and the low complexity of the methods demonstrate the strength of the proposed heuristic solution procedures. 相似文献
169.
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces. 相似文献
170.
The product customisation trend has an unprecedented impact on manufacturing companies, as the ever-increasing number of product variants and the enlarged pool of cooperating partners vastly increase the feasible alternative supply chain configurations. In terms of decision theory, this is translated to enormous search spaces. For tackling these NP-hard problems, metaheuristic optimisation methods are utilised, which provide a trade-off between the quality of solutions and the computation time. This research work describes the modelling and solving of two supply chain configuration problems using the Simulated Annealing and Tabu Search methods. The performance of the identified solutions in terms of optimisation of multiple conflicting criteria, is compared against the results obtained from a custom Intelligent Search Algorithm and an Exhaustive enumerative method. The algorithms are developed into a web-based software platform. The approach is validated through real life applications to case studies from the automotive and CNC laser welding machine building industries. 相似文献