OPTIMISASI ECONOMIC DISPATCH MENGGUNAKAN ANT COLONY OPTIMIZATION PADA SISTEM IEEE 26 BUS

Economic dispatch akan diaplikasikan  pada sistem IEEE 26 Bus dengan menggunakan metoda Ant Colony Optimization. Ant Colony Optimization merupakan salah satu teknik komputasi yang menyelesaikan suatu permasalahan optimisasi berdasarkan perilaku se-kelompok semut untuk mencari jalur terpendek dari sarang ke suatu sumber makanan. Dari hasil pengujian terbukti bahwa Ant Colony Optimization mampu menghasilkan biaya pembangkitan yang lebih optimal jika dibandingkan dengan metoda Lagrange Multiplier. Ant Colony Optimization mampu meminimalkan biaya pembangkitan sebesar 0,796 $ / jam dan meminimalkan rugi-rugi transmisi sebesar 0,043 M

OPTIMISASI ECONOMIC DISPATCH MENGGUNAKAN ANT COLONY OPTIMIZATION PADA SISTEM IEEE 26 BUS

Economic dispatch akan diaplikasikan  pada sistem IEEE 26 Bus dengan menggunakan metoda Ant Colony Optimization. Ant Colony Optimization merupakan salah satu teknik komputasi yang menyelesaikan suatu permasalahan optimisasi berdasarkan perilaku se-kelompok semut untuk mencari jalur terpendek dari sarang ke suatu sumber makanan. Dari hasil pengujian terbukti bahwa Ant Colony Optimization mampu menghasilkan biaya pembangkitan yang lebih optimal jika dibandingkan dengan metoda Lagrange Multiplier. Ant Colony Optimization mampu meminimalkan biaya pembangkitan sebesar 0,796 $ / jam dan meminimalkan rugi-rugi transmisi sebesar 0,043 M

Multi Objectives Fuzzy Ant Colony Optimization Design of Supply Path Searching

One of problem faced in supply chain management is path searching. The best path depend not only on distance, but also other variables, such as: the quality of involved companies, quality of delivered product, and other value resulted by quality measurement. Commonly, the ant colony optimization could search the best path that has only one objective path. But it would be difficult to be adopted, because in the real case, the supply path has multi path and objectives (especially in palm oil based bioenergy supply). The objective of this paper is to improve the ant colony optimization for solving multi objectives based supply path problem by using fuzzy ant colony optimization. The developed multi objectives fuzzy ant colony optimization design was explained here, that it was used to search the best supply path. Salah satu masalah yang dihadapi dalam Supply Chain Management adalah pencarian jalur. Jalur terbaik tidak hanya tergantung pada jarak, tetapi juga variabel lain, seperti: kualitas Perusahaan yang terlibat, kualitas produk yang dikirimkan, dan nilai lain yang dipengaruhi oleh pengukuran kualitas. Umumnya, Ant Colony Optimization bisa mencari jalur terbaik yang hanya memiliki satu jalur objektif. Tapi akan sulit untuk diadopsi, karena dalam kasus nyata, jalur supply memiliki banyak jalur dan tujuan (khususnya pasokan minyak kelapa sawit berbasis bioenergi). Tujuan dari penelitian ini adalah untuk meningkatkan Ant Colony Optimization dalam menyelesaikan masalah jalur supply dengan menggunakan Fuzzy Ant Colony Optimization. Tujuan pengembangan Fuzzy Ant Colony Optimization dijelaskan disini, yaitu digunakan untuk mencari jalur supply terbaik

Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

Unit Commitment or generator scheduling is one of complex combination issues aiming to obtain the cheapest generating power total costs. Ant Colony Optimization is proposed as a method to solve Unit Commitment issues because it has a better result convergence according to one of journals that reviews methods to solve Unit Commitment issues. Ant Colony Optimization modification into Nodal Ant Colony Optimization as well as addition of several elements are also conducted to overcome Ant Colony Optimization limitations in resolving Unit Commitment issues. Nodal Ant Colony Optimization simulations are then compared with Genetic Algorithm and Simulated Annealing methods which previously has similar simulations. Reliability index combination in a form of Loss of Load Probability and Expected Unserved Energy are also added as reliability constraints in the system. Comparison of three methods shows that Nodal Ant Colony Optimization is able to provide better results up to 0.08% cheaper than Genetic Algorithm or Simulated Annealing methods

Ant Colony Optimization Algorithms for Shortest Path Problems - Java implementation

Tato diplomová práce se zabývá hledáním nejkratší cesty pomocí mravenčích algoritmů. V teoretické části jsou popsány mravenčí algoritmy. V praktické části jsou zvoleny tyto algoritmy pro návrh a implementaci hledání nejkratší cesty v jazyce Java.This diploma thesis deals with ant colony optimization for shortest path problems. In the theoretical part it describes Ant Colony Optimization. In the practical part ant colony optimization algorithms are selected for the design and implementation of shortest path problems in the Java.

Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima