Analisis dan Penerapan Algoritma Particle Swarm Optimization (PSO) pada Optimasi Penjadwalan Sumber Daya Proyek Analysis and Implementation of Particle Swarm Optimization (PSO) Algorithm on the Optimization of Project Resource Scheduling

ABSTRAKSI: Penjadwalan sumber daya (resource scheduling) merupakan salah satu perencanaan operasional proyek yang didalamnya terdapat proses mengidentifikasi jenis dan jumlah sumber daya sesuai dengan jadwal keperluan yang telah ditetapkan. Kebutuhan sumber daya untuk masing-masing satuan waktu bisa berbeda, sehingga ada kemungkinan terjadi fluktuasi kebutuhan sumber daya. Oleh karena itu, fluktuasi yang tinggi selama perjalanan proyek harus dihindari, dengan kata lain diperlukan perataan penggunaan sumber daya (resource leveling) sepanjang waktu proyek. Permasalahan optimasi perataan penggunaan sumber daya merupakan masalah yang sudah umum dan telah dipelajari dalam waktu yang lama, namun perlu dicari metodologi atau pendekatan teknis yang memadai dan sampai saat ini telah berkembang beberapa solusi alternatif yang ditawarkan. Pada Tugas Akhir ini, dibahas penerapan algoritma particle swarm optimization (PSO), pada optimasi penjadwalan sumber daya proyek. Hasil penelitian menunjukkan bahwa algoritma particle swarm optimization dapat diterapkan pada permasalahan optimasi perataan penggunaan sumber daya. Keefektifan algoritma ini didemonstrasikan dengan beberapa studi kasus. Dari test yang telah dilakukan menunjukkan bahwa pendekatan ini dapat memberikan solusi yang bagus dengan fluktuasi yang minimal.Kata Kunci : penjadwalan sumber daya proyek, fluktuasi, Resource Leveling, PSOABSTRACT: Resource scheduling is one of project operational planning which there is have process identifies resources amount and type according to the activity that scheduled. Requirements of resources for each times can different, so maybe there is fluctuation of need resources. So, a high of fluctuation during project tour must be avoid, i.e.needed resource leveling during project time. The problem of resource leveling optimization is a common problem in have studied at long time, but must be search a new methodology or several heuristic in order to produce the optimal solutions of the problem. In this final assignment, we discuss an implementation of the particle swarm optimization (PSO), on the optimization of project resource scheduling. Final result shows that particle swarm optimization can be implement on the optimization of resource leveling problem. The effectiveness of this heuristic is demonstrated with case studies. Preliminary test shows that this approach can give a good solution with minimal fluctuation.Keyword: project resource scheduling, fluctuation, resource leveling, PS

Swarm Intelligence Optimization Algorithms and Their Application

Swarm intelligence optimization algorithm is an emerging technology tosimulate the evolution of the law of nature and acts of biological communities, it has simple and robust characteristics. The algorithm has been successfully applied in many fields. This paper summarizes the research status of swarm intelligence optimization algorithm and application progress. Elaborate the basic principle of ant colony algorithm and particle swarm algorithm. Carry out a detailed analysis of drosophila algorithm and firefly algorithm developed in recent years, and put forward deficiencies of each algorithm and direction for improvement

Optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on Particle Swarm Optimization Algorithm

Aiming at the imbalance of seasonal agricultural machinery operations in different regions and the low efficiency of agricultural machinery, an experiment is proposed to use particle swarm algorithm to plan agricultural machinery paths to solve the current problems in agricultural machinery operations. Taking the harvesting of autumn soybeans at Jianshan Farm in Heilongjiang Reclamation Area as the experimental object, this paper constructs the optimization target model of the maximum net income of farm machinery households, and uses particle swarm algorithm to carry out agricultural machinery operation distribution and path planning gradually. In this paper, by introducing 0 - 1 mapping, the improved algorithm adopts continuous decision variables to solve the optimization of discrete variables in agricultural machinery operations. The test results show that the particle swarm algorithm can realize the optimal allocation of agricultural machinery path, and the particle swarm algorithm is scientific and explanatory to solve the agricultural machinery allocation problem. This research can provide a scientific basis for farm agricultural machinery allocation and decision analysis

A new approach to particle swarm optimization algorithm

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Diminution of Real Power Loss by Hybridization of Particle Swarm Optimization with Extremal Optimization

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide variety of hard optimization problems. To overcome the limitation of PSO, this paper proposes a novel hybrid algorithm, called hybrid PSO-EO algorithm, through introducing EO to PSO. The hybrid approach elegantly combines the exploration ability of PSO with the exploitation ability of EO. The proposed approach is shown to have superior performance and great capability of preventing pre- mature convergence across it comparing favourably with the other algorithms. We demonstrated that our proposed HPSOEO (hybrid particle swarm optimization – Extremal optimization) presents a better performance when compared to the other algorithms. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that HPSOEO is more efficient than others for solution of single-objective Optimal Reactive Power Dispatch problem. Keywords: Modal analysis, optimal reactive power, Transmission loss, particle swarm, Particle swarm optimization, Extremal optimization, Numerical optimization, Metaheuristic

Economic and Emission Dispatch using Whale Optimization Algorithm (WOA)

This paper work present one of the latest meta heuristic optimization approaches named whale optimization algorithm as a new algorithm developed to solve the economic dispatch problem. The execution of the utilized algorithm is analyzed using standard test system of IEEE 30 bus system. The proposed algorithm delivered optimum or near optimum solutions. Fuel cost and emission costs are considered together to get better result for economic dispatch. The analysis shows good convergence property for WOA and provides better results in comparison with PSO. The achieved results in this study using the above-mentioned algorithm have been compared with obtained results using other intelligent methods such as particle swarm Optimization. The overall performance of this algorithm collates with early proven optimization methodology, Particle Swarm Optimization (PSO). The minimum cost for the generation of units is obtained for the standard bus system

Optimization of PID Controller Based on PSOGSA for an Automatic Voltage Regulator System

AbstractThis paper presents an optimal Proportional Integral Derivate (PID) controller design for an Automatic Voltage Regulator (AVR) system using a new hybrid devised from the Particle Swarm Optimization and the Gravitational Search Algorithm (PSOGSA). The transient response analysis and bode analysis were considered to show the effectiveness of the design technique. Moreover, the comparison of the results between the proposed approach and other techniques such as the Ziegler-Nichols (ZN) tuning method, the Particle Swarm Optimization (PSO) tuning method and the Many Optimizing Liaisons (MOL) tuning method have been given. According to the analysis, the proposed PSOGSA algorithm gives better results than other techniques for the AVR system

Dual Target Optimization of Two-Dimensional Truss Using Cost Efficiency and Structural Reliability Sufficiency

The main contribution of this study is to open a discussion regarding the structural optimization associated with the cost efficiency and structural reliability sufficiency consideration. To do so, several various optimization approaches are investigated to deliberate both cost and reliability concerns. Particularly, particle swarm optimization is highlighted as a reliable optimization approach. Accordingly, an illustrative example is rendered to compare the feasibility of the considered optimization approaches. The feasibility of the investigated approaches is evaluated using the cost and reliability analysis. For the considered example, it was observed that the PSO optimization algorithm has multiple advantages such as easy realization, fast convergence, and promising performance in nonlinear performance optimization. The PSO optimization algorithm can be successfully applied in various fields of civil engineering. This popularity is due to the understandable performance of the PSO as well as its simplicity. In this paper, first, the literature on the subject has been described by two-dimensional truss analysis using the finite element method and optimized using the PSO particle swarm algorithm. A comparison of the results with this reference indicates the accuracy of this particle swarm algorithm in truss optimization. Indeed, this study ignites two main insights in structural optimizations assessment. The first illustration is related to how to establish a framework for structural system reliability analysis associated with the different degrees of indeterminacies. And the second illustration is related to making a decision problem concerning the structural optimization while both cost and reliability metric are two main parameters for the construction point of the view