<![CDATA[Recent developments in Wireless Sensor Networks (WSN) focus on scalability and reliability. This research addresses the challenge of improving reliability in WSNs through optimal relay placement and multipath topology design. A heuristic method with a Multi-Objective Optimization (MOO) approach is proposed to solve this problem. The proposed method integrates a modified Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) characteristics. The hybrid approach aims to minimize the number of relays and associated communication costs while maintaining network reliability. The method encodes relay positions and quantities into GA chromosomes that are updated by mutation, crossover, and PSO-inspired particle motion. Simulations are performed in a simplified square area with twenty randomly placed sensors, a hundred and thirty-two arranged relays, and a single sink node. As a result, the simulation generated two multipath topologies that offer unique advantages. The first emphasizes relay efficiency (61 relays, with 2096 costs), while the second ensures lower communication costs (64 relays, 1832 costs). Comparisons with alternative algorithms, including Dijkstra, A-star, GA, and PSO, prove the superiority of the proposed approach. The optimum results obtained with a composition of 95% GA and 5% PSO, outperform alternative algorithms in terms of relay efficiency and communication cost. This research contributes to the field by providing a robust solution for designing reliable multipath WSNs with a minimum number of relays.]]>
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