Abstract
The swarm intelligence and evolutionary methods are commonly utilized by researchers in solving the difficult combinatorial and Non-Deterministic Polynomial (NP) problems. The N-Queen problem can be defined as a combinatorial problem that became intractable for the large ‘n’ values and, thereby, it is placed in the NP class of problems. In the present study, a solution is suggested for the N-Queen problem, on the basis of the Meerkat Clan Algorithm (MCA). The problem of n-Queen can be mainly defined as one of the generalized 8-Queen problem forms, for which the aim is placing 8 queens in a way that none of the queens has the ability of killing the others with the use of the standard moves of the chess queen. The Meerkat Clan environment is a directed graph, called the search space, produced for the efficient search of valid n-queens’ placement, in a way that they do not cause harm to one another. This paper also presents the development of an intelligent heuristic function which is helpful to find the solution with high speed and effectiveness. This study includes a detailed discussion of the problem background, problem complexity, Meerkat Clan Algorithm, and comparisons of the problem solution with the Practical Swarm Optimization (PSO) and Genetic Algorithm (GA. It is an entirely review-based work which implemented the suggested designs and architectures of the methods and a fair amount of experimental results.
Highlights
A swarm can be defined as a large amount of simple and homogenous factors that locally interact amongst one another, and with their environment, without any central regulation for allowing the emergence of a general interesting behavior
The N-Queen problem is represented by placing ‘n’ queens on a chessboard in a way that none of the queens has the ability of killing the rest with the use of the standard moves of the chess queen
From the earlier explanations about the Meerkat animal-inspired MCA, the followings are the steps for the MCA to solve the N-Queen problem: Initialization the parameters; Creating clan of boards randomly; Calculating the fitness for all boards in clan; Choosing the optimal board as 'sentry'; Dividing the boards to 2 groups; Repeat
Summary
A swarm can be defined as a large amount of simple and homogenous factors that locally interact amongst one another, and with their environment, without any central regulation for allowing the emergence of a general interesting behavior. This algorithm begins by fixed steps, which are utilized to initialize the parameter values, and flows with the number of the iterated steps for discovering the optimal solution to the problem [4]. The results showed that a conceptually quite simple heuristic function (like a case in which the neighborhood includes n-tuples differed from current solution in the positions of the two queens) might address this difficult non-deterministic polynomial problem [12].
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