improved real-coded genetic algorithm for planar maximal covering location problem

Facility location is a strategic optimization problem in organizational decision making. maximal coverage problems are a class of widely used facility location problems. in this paper, pmclp problem is considered, and a real-coded genetic algorithm is introduced to attain near-optimal answers for pmclp in short time. initial solutions of the problem are obtained by a randomized greedy heuristic. these answers are then evolved by innovative demand-based crossover operator and a virtual-force mutation operator in successive iterations. to evaluate effectiveness and efficiency of the proposed method and its genetic operators, several medium and large-scale problems are solved in our experiments. results are then compared with an exact algorithm. the experimental results show that the proposed real-coded genetic algorithm provides near-optimal answers to large-scale problems in tolerable time.