https://www.journal.armestudio.co.id/index.php/AEJ/issue/feed 2023-12-09T11:13:15+00:00 Open Journal Systems <p>ASEAN Engineering Journal (AEJ) is an official publication of the JICA Project for AUN/SEED-Net as a fruit of collaboration with the main support from the Government of Japan through Japan International Cooperation Agency (JICA), Member Governments from 10 ASEAN countries and 26 Member Institutions (MIs) selected from leading institutions in engineering field with support from 14 leading Japanese Universities.</p> <p>Over the years, AEJ developed a strong international academic network from extensive human resources established by network of professionals and continuous collaborations via the journal. </p> <p>ASEAN Engineering Journal is published online with a frequency of four (4) issues per year (March, June, September, and December). A double-blind peer review system is adopted to ensure the transparency and integrity of the review process.</p> <p>The journal is open access and does not charge any publication fee for AUN/SEED.net Member of Institution in ASEAN.</p> https://www.journal.armestudio.co.id/index.php/AEJ/article/view/1 2023-12-09T11:13:15+00:00 Devi Anggara deviwillieamunggula@googlemail.com

<p>This study was conducted to promote a new adaptive cone algorithm (ACA) algorithm. ACA is a metaheuristic technique based on swarm intelligence. ACA contains three steps. Each agent moves closer to the global reference in the first step. Then, each agent searches for a better solution around the current solution in the second step. The global reference searches for better solutions around it in the third step. This algorithm is named cone because the local space size declines linearly during the iterative process. ACA introduces a new adaptability model to improve the exploration strategy when a better solution cannot be achieved. It is conducted by enlarging the local solution space. ACA is challenged to find the final solution for theoretical and practical problems. The 23 functions are chosen as theoretical optimization problems. The portfolio optimization problem is selected as the practical problem. ACA is compared with five algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), marine predator optimization (MPA), average subtraction-based optimizer (ASBO), and pelican optimization algorithm (POA). The result shows that ACA is competitive in finding the optimal solution for 23 functions and outperforms all sparing algorithms in achieving the highest total capital gain in tackling the portfolio optimization problem. ACA is superior to PSO, GWO, MPA, ASBO, and POA in solving 20, 11, 13, 4, and 21 functions, respectively. In the future, ACA can be implemented in solving various practical optimization problems.</p>

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