Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method

This paper proposes an approach based on the Symbiotic Organism Search (SOS) for optimal determining sizing, siting, and number of Distributed Generations (DG) in distribution systems. The objective of the problem is to minimize the power loss of the system subject to the equality and inequality constraints such as power balance, bus voltage limits, DG capacity limits, and DG penetration limit. The SOS approach is defined as the symbiotic relationship observed between two organisms in an ecosystem, which does not need the control parameters like other meta-heuristic algorithms in the literature. For the implementation of the proposed method to the problem, an integrated approach of Loss Sensitivity Factor (LSF) is used to determine the optimal location for installation of DG units, and SOS is used to find the optimal size of DG units. The proposed method has been tested on IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the SOS algorithm have been compared to those of other methods in the literature. The simulated results have demonstrated that the proposed SOS method has a very good performance and effectiveness for the problem of optimal placement of DG units in distribution systems

Comparative and comprehensive study of linear antenna arrays’ synthesis

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm

Hybrid scheduling algorithms in cloud computing: a review

Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms

An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning

Mobile robotic platforms are an indispensable tool for various scientific and industrial applications. Robots are used to undertake missions whose execution is constrained by various factors, such as the allocated time or their remaining energy. Existing solutions for resource constrained multi-robot sensing mission planning provide optimal plans at a prohibitive computational complexity for online application [1],[2],[3]. A heuristic approach exists for an online, resource constrained sensing mission planning for a single vehicle [4]. This work proposes a Genetic Algorithm (GA) based heuristic for the Correlated Team Orienteering Problem (CTOP) that is used for planning sensing and monitoring missions for robotic teams that operate under resource constraints. The heuristic is compared against optimal Mixed Integer Quadratic Programming (MIQP) solutions. Results show that the quality of the heuristic solution is at the worst case equal to the 5% optimal solution. The heuristic solution proves to be at least 300 times more time efficient in the worst tested case. The GA heuristic execution required in the worst case less than a second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and Automation Letters (RA-L

Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search

Resource leveling is used in project scheduling to reduce fluctuation in resource usage over the period of project implementation. Fluctuating resource usage frequently creates the untenable requirement of regularly hiring and firing temporary staff to meet short-term project needs. Construction project decision makers currently rely on experience-based methods to manage fluctuations. However, these methods lack consistency and may result in unnecessary waste of resources or costly schedule overruns. This research introduces a novel discrete symbiotic organisms search for optimizing multiple resources leveling in the multiple projects scheduling problem (DSOS-MRLMP). The optimization model proposed is based on a recently developed metaheuristic algorithm called symbiotic organisms search (SOS). SOS mimics the symbiotic relationship strategies that organisms use to survive in the ecosystem. Experimental results and statistical tests indicate that the proposed model obtains optimal results more reliably and efficiently than do the other optimization algorithms considered. The proposed optimization model is a promising alternative approach to assisting project managers in handling MRLMP effectively

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla

Towards a conceptual design of intelligent material transport using artificial intelligence

Reliable and efficient material transport is one of the basic requirements that affect productivity in industry. For that reason, in this paper two approaches are proposed for the task of intelligent material transport by using a mobile robot. The first approach is based on applying genetic algorithms for optimizing process plans. Optimized process plans are passed to the genetic algorithm for scheduling which generate an optimal job sequence by using minimal makespan as criteria. The second approach uses graph theory for generating paths and neural networks for learning generated paths. The Matla