Volume 30, Issue 4 (7-2023)                   RJMS 2023, 30(4): 223-235 | Back to browse issues page

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Mohammadi H, Mirzaie K. Locating Medical and Emergency Centers using the Combined Approach of Collective Intelligence based on Complex Networks. RJMS 2023; 30 (4) :223-235
URL: http://rjms.iums.ac.ir/article-1-8585-en.html
Assistant Professor, Department of Computer Engineering, Payame Noor University (PNU), Tehran, Iran. , h.mohammadi@pnu.ac.ir
Abstract:   (514 Views)
Background & Aims: One of the optimization issues is resource allocation. This means that resources should be allocated in different places so that the cost of allocating the problem is reduced. In addition to its theoretical aspect, the issue of resource allocation can be used in various practical issues such as the timing of project resources, the distribution of foodstuffs in city-level supermarkets, the allocation of medical and emergency centers in a city, etc. Collective intelligence algorithms can be used to solve such problems. In the last decade, various collective intelligence algorithms have been used to solve complex problems such as resource allocation. In this article, the PSO algorithm and its improved variants are used. In the PSO algorithm, it refers to each of the components of a particle. To continue moving, each particle uses its previous experience and that of its neighbors. Every particle in space is moving at a speed. At every moment, this movement leads to the change of the current position of each particle in space. In the PSO algorithm, the communication between particles takes place in two parts of the algorithm. One is the personal best fit of each particle and the other is the best global fit. All the particles that are generated at the beginning of the algorithm as the initial population. Then the fit of each particle is calculated and stored as its personal best fit. In the same way, in updating the position of each particle, this value is compared with the fit of the same particle in the new position, if this fit is better than the previous position of the particle, it is stored as the best personal fit of the same particle. If there is a change in the personal best fit, the fit of this solution is compared with the global best fit. Then, the global best fit is updated.
Methods: In this article, the particle swarm optimization algorithm is used to solve various allocation problems. To increase the efficiency, the particle swarm optimization algorithm has been improved with the help of local search. To better strengthen the communication between particles, the concept of a complex network has been used. The status of that particle in the complex network is effective for deciding on the choice of particles. The degree criterion is considered for selecting solutions in the complex network. Two groups of optimization problems have been used to evaluate the results. The first group is different standard and theoretical problems from the QAPLib library for solving the quadratic assignment problem. The second group is also a problem with the location of medical and emergency centers, considering the probability of the center's failure according to real-world conditions. In this issue, it is discussed that according to the criteria of location, cost of construction and reconstruction, and the distance of medical and emergency centers from each other in different population spots, urban medical and emergency centers should be established in which population spot.
Results: In order to compare and evaluate different proposed approaches for resource allocation, two groups of different problems have been used. The first group of standard problems from the QAPLib library is for quadratic assignment. The second group is solving the problem of locating medical and emergency centers in different population spots of the city. The evaluation results of both groups show the average cost and error percentage of the proposed memetic particle swarm optimization algorithm along with the complex network with degree measure compared to the memetic particle swarm optimization algorithm and the basic particle swarm optimization algorithm. For example, to solve the Tia60 problem, the memetic mesh particle swarm optimization algorithm, the memetic algorithm, and the base algorithm have 4.55%, 4.59%, and 8.15% error percentages, respectively.
Conclusion: The results of the implementation of the first to third algorithms for locating medical and emergency centers with 100 repetitions and an initial population size of 50 shows. The memetic degree complex grid particle swarm optimization algorithm has reached the optimal value faster than the standard PSO algorithm and PSO with local search. The third method is more suitable than the first and second method for locating medical and emergency centers. The proposed optimization algorithm of memetic particle swarm with the complex network with degree scale for locating medical and emergency centers has a lower average cost and error percentage than the memetic particle swarm optimization algorithm and basic particle algorithm. One of the problems of optimizing the allocation of resources is the location of medical and emergency centers in urban population spots. This problem has been solved with the help of three different meta-heuristic approaches. To allocate medical and emergency centers, a particle swarm optimization algorithm has been used. Then this algorithm has been converted into a memetic algorithm with the help of the proposed local search. To create a connection between particles, the concept of a complex network has been used. In the third proposed method, in addition to the particle swarm optimization algorithm, along with local search, a complex network with a degree scale is used. To evaluate the results, two groups of problems have been used. The first group has been used to solve various quadratic assignment problems from the QAPLib library. In the second group, the practical problem of locating medical and emergency centers of different population spots in the city is considered. To solve both groups, the proposed memetic complex network particle swarm optimization algorithm has a lower average cost and error percentage than the memetic particle swarm optimization algorithm and the basic particle swarm optimization algorithm. To improve the performance of the proposed algorithms, other metrics can be used in the complex network or he used other local search algorithms such as refrigeration simulation, and forbidden search.
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Type of Study: Research | Subject: Biotechnology

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