A cascade evolutionary algorithm for the bodyguard allocation problem

Graphical abstractDisplay Omitted HighlightsThis paper address the bodyguard allocation problem (BAP) through an evolutionary approach.The paper proposes a two-stage cascade evolutionary algorithm that outperforms the state-of-the-art algorithm, known as CBAP, for this problem.The paper presents the computational experiments of the evaluation of both algorithms. The bodyguard allocation problem (BAP) is an optimization problem that illustrates the behavior of processes with contradictory individual goals in some distributed systems. The objective function of this problem is the maximization of a parameter called the social welfare. Although the main method proposed to solve this problem, known as CBAP, is simple and time efficient, it lacks the ability to generate a diverse set of solutions, which is one of the most important feature to improve the chances to reach the global optimum. To overcome this drawback, we address the BAP with an evolutionary algorithm, the EBAP. Later, we take advantage of the best properties of both algorithms, EBAP and CBAP, to generate a two-stage cascade evolutionary algorithm called FFC-BAP. Extensive experimental results show that the algorithm FFC-BAP outperforms both the EBAP and the CBAP, in terms of quality of solutions.

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