Approximate Muscle Guided Beam Search for Three-Index Assignment Problem

Abstract

Abstract. As a well-known NP-hard problem, the Three-Index Assignment Problem (AP3) has attracted lots of research efforts for developing heuristics. However, existing heuristics either obtain less competitive solutions or consume too much running time. In this paper, a new heuristic named Approximate Muscle guided Beam Search (AMBS) is developed to achieve a good trade-off between solution quality and running time. By combining the approximate muscle with beam search, the solution space size can be significantly decreased, thus the time for searching the solution can be sharply reduced. Extensive experimental results on the benchmark indicate that the new algorithm is able to obtain solutions with competitive quality and it can be employed on instances with large-scale. Work of this paper not only proposes a new efficient heuristic, but also provides a promising method to improve the efficiency of beam search.

Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons

“…Consequently, GWO retains three most ideal solutions attained to this point and obliges the omega wolves to bring up to date their positions to attain the ideal place in the decision space. According to [14] such a hunting behavior can be modelled in an optimization algorithm by expressing it as:…”

“…Fister et al in [11] presented a concise review of bio-inspired algorithms highlighting the different types of bio-inspired algorithms in literature. Lately, Flower Pollination Algorithm (FPA) [12], Chicken Swarm Optimization (CSO) [13], Approximate Muscle Guided Beam Search (AMGBS) [14], Magnetotactic Bacteria Optimization based on Moment Migration (MBOMM) [15], Elephant Herding Optimization [16], Bull optimization algorithm [17] and modified imperialist competitive algorithm with invasive weed optimization (MICA-IWO) were proposed. Depicted in Figure 1 are the different SI-based algorithms available in literature.…”

Multiple Hungarian Method for k-Assignment Problem

“…Furthermore, Huang and Lim hybridized their heuristic with a genetic algorithm. An extension of the Huang and Lim heuristic [17] to the multidimensional assignment problem was done in [18,19], while [20] developed another new heuristic named Approximate muscle guided beam search, using the local search of [17]. Karapetyan and Gutin [21] devised a memetic algorithms with an adjustable population size technique and with the same local search as Huang and Lim for the case of 3-AP.…”