Search

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]

Iterated Local Search (ILS) is one of the classical metaheuristics that is frequently used for solving the Quadratic Assignment Problem (QAP), a commonly studied NP-hard combinatorial optimization problem. Despite the effectiveness shown by the ILS when solving the QAP, many possible enhancements can still be implemented, based on studies conducted on other combinatorial problems, for example vertex coloring problem, vehicle routing problem, network design optimization, and many others. Among possible approaches for improving the ILS are by implementing adaptive ILS and multi-start strategy. Adaptive ILS is implemented by dynamically changing the perturbation strength of ILS during the run, which focus on developing a robust algorithm that can solve a wide range of instances. Multi-start strategy on the other hand, focus on applying restart or diversification during ILS run to prevent early stagnation. Adaptive ILS with frequent and large perturbation strength change has shown the best overall result when it has obtained the lowest deviation to the best-known solution compared to others. Implementation of Multi-start strategy showed bad performance, except on Taillard75e instance, in which it frequently found the best-known solution compared to others. Both adaptive and multi-start strategy have shown interesting results that may be further studied for ILS improvement. [ABSTRACT FROM AUTHOR]