Orthogonal methods based ant colony search for solving continuous optimization problems

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others

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