A phase-only adaptive processing based on direct data domain using hybrid genetic algorithms is presented. Considering practical application of the complex weight requires complex hardware system, the new algorithm ph...
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ISBN:
(纸本)9781467321013;9781467321006
A phase-only adaptive processing based on direct data domain using hybrid genetic algorithms is presented. Considering practical application of the complex weight requires complex hardware system, the new algorithm phase-only adaptive processing based on direct data domain is presented to reduce the complexity requirement of system implementation. Then a hybrid genetic algorithms combined genetic algorithm with local search algorithm is used to solve the phase-only adaptive weights. Experimental results show that the phase-only weights solved by a hybrid genetic algorithm not only form deep null in the direction of interferences, but also estimate the magnitude of the target signal accurately. The proposed algorithm achieves the purpose of interference suppression and detecting targets, as well as reduces the complexity of hardware implementation of the system greatly for engineer implement easily.
This paper introduces a near-optimal path planning for industrial robot for multiple point tasks. For instance, spot welding, drilling, screwing and inspection with camera are popular multiple point work for industria...
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ISBN:
(数字)9781728134581
ISBN:
(纸本)9781728134581
This paper introduces a near-optimal path planning for industrial robot for multiple point tasks. For instance, spot welding, drilling, screwing and inspection with camera are popular multiple point work for industrial robots. How to decide the sequence of robot motions is important issue for factory automation because it directory influences to system productivity. Though getting optimal tour is not easy because this is kind of Traveling Salesman Problem (TSP) that is almost impossible to solve in polynomial time. In TSP the cost between two cities is Oven by geometrical distance though in robotics time for moving between two task points is gets cost. This article adopts heuristic algorithm that is often used to solve TSP to solve about 50 task points path planning for industrial robot. As a result, the algorithm gives under PA gap solution against best known solutions. For actual robot tasks, it generates about 6 to 10% efficient results compared with human task planner's path.
In recent years, edge computing has emerged as a promising solution in the field of network computing. This architecture ensures the availability of distributed computing resources located closer to end-users and IoT ...
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ISBN:
(纸本)9798350377873;9798350377866
In recent years, edge computing has emerged as a promising solution in the field of network computing. This architecture ensures the availability of distributed computing resources located closer to end-users and IoT devices. However, resource scheduling remains a significant challenge in edge computing, requiring effective strategies to optimize resource utilization and ensure efficient task allocation. In this paper, we propose two hybrid approaches that combine the NawazEnscore-Ham (NEH) algorithm with localsearch and Greedy Random Adaptive search Procedure (GRASP) algorithm with localsearch for modeling and solving data traffic in distributed edge computing environments (DPSDEC). Through extensive evaluations, we consistently observe that the NEH algorithm outperforms GRASP, delivering minimized makespan and generating efficient schedules. Moreover, the NEH algorithm performs very well in less complex situations and maintains this advantage even in larger and more complex problems.
Let G = (V, E) be a complete graph with set of nodes V = {1, . . .,} g and edge set E = {1, . . ., m} representing a wireless sensor network. In this paper, we consider the problem of finding a minimum cost spanning t...
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ISBN:
(纸本)9789897585296
Let G = (V, E) be a complete graph with set of nodes V = {1, . . .,} g and edge set E = {1, . . ., m} representing a wireless sensor network. In this paper, we consider the problem of finding a minimum cost spanning tree backbone formed with p is an element of Z(+) out of n nodes where p < n in such a way that the n - p remaining nodes of G are connected to the leaf nodes of the backbone structure at minimum connectivity cost. Notice that this problem arises as a combination of two classical combinatorial optimization problems, namely the p-Median and spanning tree problems. We propose two mixed-integer linear programming (MIP) formulations for this problem as well as a localsearch heuristic. The proposed models and algorithm can be used as a reference source for comparison purposes when designing future network protocols. We consider complete graph instances with Euclidean and random uniform costs. Our preliminary numerical results indicate that one of the proposed models performs slightly better than the other one in terms of solution quality and CPU times obtained with the Gurobi solver. Finally, the proposed heuristic allows one to obtain near-optimal solutions in remarkably less CPU time compared to the MIP models.
The harmony search (HS) algorithm is one of the recent evolutionary computation techniques to solve optimization problems. To make it applicable for lot-streaming flow shop problems, a discrete variant of the HS algor...
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ISBN:
(纸本)9781424481262
The harmony search (HS) algorithm is one of the recent evolutionary computation techniques to solve optimization problems. To make it applicable for lot-streaming flow shop problems, a discrete variant of the HS algorithm (DHS) with job permutations representation is proposed. In the proposed DHS algorithm, a new improvisation scheme is designed to generate feasible job sequences. A local search algorithm based on the insert neighborhood structure is fused to stress the further enhancement capability of the algorithm proposed whereas a restart scheme is employed to avoid the stagnation of the evolution. Extensive computational simulations and comparisons are provided, which demonstrate the effectiveness of the proposed DHS against the best performing algorithms from the literature.
Artificial Bee Colony (ABC) is a popular swarm intelligence based approach used to solve nonlinear and complex optimization problems. It is a simple to implement and swarm based probabilistic algorithm. As the case of...
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ISBN:
(纸本)9781479931408
Artificial Bee Colony (ABC) is a popular swarm intelligence based approach used to solve nonlinear and complex optimization problems. It is a simple to implement and swarm based probabilistic algorithm. As the case of other swarm based algorithms, ABC is also computationally expensive due to its slow nature of search process. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. Therefore, to balance the exploration and exploitation characteristics of the ABC, Shrinking Hyper-Sphere based localsearch approach is developed and hybridized with in the ABC solution search process. The proposed algorithm is named as Shrinking Hyper-Sphere based ABC (SHABC). The experiments over 14 well known benchmark functions of complex in nature, show that the SHABC algorithm perform better than the original version of ABC and its latest variant, namely Modified ABC (MABC) in most of the experiments.
The performance of network intrusion detection systems based on machine learning techniques largely depends on the selected features. However, choosing the optimal subset of features from a given feature set requires ...
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ISBN:
(纸本)9781467365376
The performance of network intrusion detection systems based on machine learning techniques largely depends on the selected features. However, choosing the optimal subset of features from a given feature set requires extensive computing resources. To tackle this problem we propose an optimal feature selection algorithm based on a local search algorithm. In order to evaluate the performance of our proposed algorithm, comparisons with a feature set composed of all 41 features are carried out over the NSL-KDD data set using a multi-layer perceptron.
Balancing diversity and convergence seems to be a difficult task when solving multi-objective optimization problems (MOPs). For addressing this issue, researchers' interest has been drawn to hybrid approaches sinc...
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Balancing diversity and convergence seems to be a difficult task when solving multi-objective optimization problems (MOPs). For addressing this issue, researchers' interest has been drawn to hybrid approaches since this cooperation allows benefiting from the advantages of both approaches and gains better trade-offs overall. Considering such fact, this paper aims at introducing a hybrid approach as a synergy of a multi-directional Ant Colony Optimization algorithm with a localsearch method based on a weighted version of epsilon Indicator using a self-adaptive neighborhood operator coined as Indicator Weighted Based localsearch with Ant Colony Optimization (IWBLS/ACO) to handle the knapsack problem within the multi-objective framework. In IWBLS/ACO, initial solutions are created by the ant colony. Then, the enhancement phase is ensured by the localsearch procedure. The algorithm is evolving based on different configurations of the epsilon quality indicator through different weight vectors. Moreover, we propose in this work, a novel self-adaptive neighborhood operator which changes automatically and dynamically as the IWBLS algorithm runs. The proposed IWBLS/ACO was tested on widely used Multi-objective Multidimensional Knapsack Problem (MOMKP) instances and compared with powerful state-of-the-art algorithms. Experimental results highlight that the proposed approach can lead to finding a good compromise between exploration and exploitation. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.
One of the most important features in smart grid is power system self-healing and power quality improvement. Power quality monitoring is essential to realize this feature. Installing power quality monitors (PQM) in ev...
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ISBN:
(纸本)9781479949342
One of the most important features in smart grid is power system self-healing and power quality improvement. Power quality monitoring is essential to realize this feature. Installing power quality monitors (PQM) in every component of the power network is not feasible due to economic reasons. So how to find the optimal number and locations of power quality monitors while maintaining system observability becomes an important problem. The major contribution of this paper includes providing the model for PQM optimization problem considering both system observability and fault location constraints. The model is then formulized as an integer linear problem and reduced to a group of k-median decision problems. A local search algorithm is proposed to solve the problem. The IEEE 14 bus network is utilized as a case study. algorithm efficiency is evaluated using Matlab tools and compared with an existing branch and bound algorithm. Experimental results show that proposed algorithm is more than an order of magnitude faster than current algorithm while maintain the accuracy of results.
After the implementation of the "Electric power alternatives" scheme, large-scale electric heat pumps (EHPs) were used to replace the previous coal-fired boilers, which makes the distribution network appears...
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ISBN:
(纸本)9781538664612
After the implementation of the "Electric power alternatives" scheme, large-scale electric heat pumps (EHPs) were used to replace the previous coal-fired boilers, which makes the distribution network appears heavy load, overload and even affected the normal power consumption of users. Based on the state-queueing control model, the following operation control of the EHP is carried out for the purpose of ensuring its safe operation: using an improved K-means clustering algorithm suitable for the control group of the EHPs, the EHPs with similar control characteristics are divided into a same control group and carrie out state-queueing control;A local search algorithm is used to optimize the load operation for the combined optimization of the sub-range operation mode of each EHP in each distribution area. An example is given to verify that the proposed operation control strategy can avoid overloading of the distribution network and ensure normal power supply and safe operation of the system when large-scale EHP loads are concentrated.
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