Indonesian Journal of Electrical Engineering and Computer Science Vol. 34, No. 1, April 2024, pp. 584~591 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v34.i1.pp584-591  584 Journal homepage: http://ijeecs.iaescore.com A new hybrid parallel genetic algorithm for multi-destination path planning problem Luthfiansyah Ilhamnanda Yusuf 1 , Aina Musdholifah 2 1 Master Program in Artificial Intelligence, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia 2 Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia Article Info ABSTRACT Article history: Received Nov 24, 2023 Revised Jan 21, 2024 Accepted Feb 4, 2024 This paper proposes a new parallel approach of multi objective genetic algorithm for path planning problem. The main contribution of this work is to reduce the population size that effect in decreasing processing times of finding the optimum path for multi destination problem. This is achieved by combining the local population of island parallel approach and global population of global parallel approach. Various experiments have been conducted to evaluate the new hybrid parallel genetic algorithm (HPGA) in solving multi-objective path planning problems. Three different test areas with 2 destinations were used to assess the performance of HPGA. Furthermore, this work compares HPGA and sequential genetic algorithm (SeqGA), as well as compared to other existing parallel genetic algorithm (GA) methods. From experimental results show that proposed HPGA outperform others, in term of processing time i.e., up to 3.6 times speedup faster, and lowest GA parameter values. This proposed HPGA can be utilized to design robots with fast and consistent path planning, especially with various obstecles. Keywords: Genetic algorithm Multi-destination Multi-objective Parallel genetic algorithm Path planning This is an open access article under the CC BY-SA license. Corresponding Author: Aina Musdholifah Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta, Indonesia Email: aina_m@ugm.ac.id 1. INTRODUCTION Finding an optimal path in an environment or path planning (PP) is the main problem in developing autonomous robots. PP algorithm allows autonomous robots to move independently without human intervention, thereby increasing efficiency in processes that can utilize autonomous robots. The simplest form of this problem is when path searching is carried out in a static environment [1]. In the real world, autonomous robots have been used in various problems, including exploring other planets and looking for victims in the search and rescue process [2]. Genetic algorithm (GA) is a metaheuristic algorithm inspired by living things and based on Darwin's survival of the fittest theory, where the best individuals will survive after undergoing long adaptation [3]. GA can be used to solve optimization problems, one of which is PP problems [4], and many studies have shown GA can be good for solving PP problems. In GA, population generation is the essential first step and dramatically influences the results. Populations in GA are solution candidates that will be optimized. The solutions are stored in the chromosomes of each individual. These chromosomes can be encoded to simplify the optimization progress. Patle et al. [5] proposes matrix-binary encoding in GA to solve PP problem and gets a comparable result to