An enhanced Moth-flame optimization algorithm for permutation-based problems

Abstract

Moth-flame optimizer (MFO) is one of the recently proposed metaheuristic optimization techniques which has been successfully used in wide range of applications. However, there are two issues with the MFO algorithm. First, as a stochastic technique, MFO may prematurely converge at some local minima during the search process. Second, the original MFO was developed for continuous search space problems and is not directly applicable to, e.g., permutation-based problems (PBP). In this paper, a novel perturbation strategy is introduced to the MFO algorithm to avoid probable local minima regions. This strategy works as follows: if the best solution obtained so far doesn't improve for a given number of consecutive iterations, the current population of solutions is perturbed using some crossover mechanism as an attempt to explore new promising neighbourhoods in the search space. In addition, smallest position values mapping technique is employed in order for the proposed, termed CrossMFO (COMFO), algorithm to be applicable to PBP problems. It is noticed that, despite these modifications, the proposed COMFO has the same time complexity order as the original MFO. Extensive simulation experiments are conducted to compare the proposed COMFO to the MFO, other enhanced versions of MFO, and some metaheuristic optimizers in solving the well-known Travelling Salesman Problem (TSP). Empirical results show that the solutions obtained using MFO are improved by a factor of 24-47% on average for large TSP instances having more than 100 cities using COMFO and can even reach 38-58% using different settings. In addition, compared to other algorithms in the literature, the proposed algorithm provides, on average, better solutions. Hence, it can be considered a promising and efficient technique for this type of problems.

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

“…MFO navigates through a process known as transverse orientation. A moth flies in this manner by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering the Moon’s distance from the moth, which is considerably long [ 17 ]. Artificial lights fool moths, causing them to exhibit certain habits.…”

“…The proposed method for the optimized band selection method is depicted in Figure 1 . The random parameter (T) accelerates convergence during generation, which varies from −1 to 1 [ 16 , 17 ]. The flow chart of the MFO algorithm is shown in Figure 2 .…”

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

“…The major drawback of this algorithm is the premature convergence at optimal local solutions during the search process. Moreover, they cannot be applied to permutation problems as it is developed for continuous search space [75]. As mentioned in Table 1, the source code of this optimization algorithm created using MATLAB for both single and multiobjective problems is made publicly available by the developer on his website at https://seyedalimirjalili.com/mfo (accessed on 1 December 2021).…”

Adaptive Grey Wolf Optimization Algorithm with Neighborhood Search Operations: An Application for Traveling Salesman Problem

“…The TSP instance used in the second scenario, as shown in Table 1, belongs to the standard library and is selected to test eil76, eli51, berlin52, kroa100, st70, oliver30, pr76, pr107, ch150, d198, tsp225, and f1417. The algorithms used in the second scenario are discrete whale optimization algorithm (DWOA) [47], discrete whale optimization algorithm with variable neighborhood search (VDWOA) [48], bat algorithm (BA) [59], GWO, moth-flame optimization (MFO) [60], and PSO [61]. In this scenario, each algorithm is performed 50 times, and the optimal solution provided is used in the comparison.…”