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Although statistical solutions to Quadratic Assignment Problems (QAP) have been accessible for some time, the increasing application of Evolutionary Algorithms (EAs) to similar challenges provides a mechanism for handling QAP with extremely broad scope. The primary contribution of the study is that it normalizes all the criteria into a single scale, irrespective of their measurement units and the demand of minimum or maximum, freeing up the researchers from the burden of meticulously quantifying the quality criteria. A tabu search algorithm for quadratic assignment issues is proposed (TSQAP), which combines the constraints of tabu search with a discrete assignment problem. The effectiveness of the suggested algorithm has been compared to certain well-established approaches, and its working principle is explained with a numerical example. After repeating the solution of each issue (8) once and recording the algorithm results, it showed its agreement, once from a total (375) a repetition of the experiment while the number of times the Artificial Bee Colony (ABC) arrived (2) as for the Firefly (FA) Algorithm giving (117), also Genetic (GA) and Particle Swarm (PSO) gives (120) and the Tabu Search algorithm (136). The proposed technique (TSQAP) is shown to yield a superior solution with low computing complexity. MATLAB was used to generate all of the findings (R2020b). [ABSTRACT FROM AUTHOR]

Quadratic Assignment Problem (QAP) is one of the famous combinatorial optimization problems in operational research field. In this problem, a set of facilities is assigned to a set of locations in form of one-to-one assignment with the goal to obtain minimum assignment cost. Since QAP is classified under NP-hard problem, a metaheuristics approach is more appropriate for finding a good solution especially when the problem size increased. Sine Cosine Algorithm (SCA) can be categorized under population-based method which shown such an excellent performance in solving various optimization problem. SCA came up with high quality solutions and performed well due to its ability in stabilizing between exploration and exploitation phases evenly during the search process for optimal solution. However, this approach is rarely used to solve discrete optimization problem such as QAP. This is because the nature of its solution search which produce continuous values for new solution based on the concept of sine cosine principle makes it challenging in solving discrete optimization problem. Hence, this study aims to modify the SCA on its acceptance criteria based on the concept of sine cosine to solve discrete optimization problem, to solve QAP and evaluate its solution performance. The instances from QAPLIB were tested. The computational results shows that the modified SCA is an effective and superior method in solving QAP when compared to the best-known solutions presented in previous studies. [ABSTRACT FROM AUTHOR]

The simplest model of multi-objective quadratic assignment problems, bi-objective quadratic assignment problem, is discussed in this paper. Weighted sum method is used in order to change the multi-objectives model into single-objective model. An algorithm inspired from the foraging strategy and gliding mechanism called squirrel search algorithm is proposed to solve this problem. The squirrel search algorithm parameters, such as number of iterations, number of flying squirrels and control parameter, predator presence probability, are observed by managing computational experiment to solve bi-objective quadratic assignment problem. The computational results show that general parameters, number of iteration and flying squirrels, affect the performance of the algorithm in solving this problem. Moreover, probability of predator presence which is as control parameter in this algorithm can bring better result when using smaller value of probability. [ABSTRACT FROM AUTHOR]