Tabu search algorithm for solving quadratic assignment problem.

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]