Binary Fruit Fly Swarm Algorithms for the Set Covering Problem

Fruit fly algorithm is a novel intelligent optimization algorithm based on foraging behavior of the real fruit flies. In order to find optimum solution for an optimization problem, fixed parameters are obtained as a result of manual test in fruit fly algorithm. In this study, it is aimed to find the optimum solution by analyzing the constant parameter concerning the direction of the algorithm instead of manual defining on initialization stage. The study shows an automated approach for finding the related parameter by utilizing grid search algorithm. According to the experimental results, it can be seen that this approach could be used as an alternative way for finding related parameter or other ones in order to achieve optimum model.

The sensor deployment is a fundamental problem in wireless sensor networks(WSN), the performance of WSN largely depends on a good sensor deployment scheme. In this paper,we present a novel sensor deployment scheme based on fruit fly algorithm(FOA) to improve the coverage rate. Each fruit fly represents a solution for sensor deployment independently, and they are given the random direction and distance for finding food using osphresis.Then we find out the fruit fly with the highest smell concentration judgment value from the fruit fly group and keep its positions,and then the fruit fly group will fly towards that position by using their sensitive vision.We have done simulations both in the ideal and obstacle areas, FOA-based sensor deployment is compared with the classic standard PSO and the novel GSO,simulation results show the effectiveness of the proposed approach.

A codebook is a combination of vectors that represents a digital image best and very useful tool for compression. Besides the well-known techniques such as Linde-Buzo-Gray, C-Means, and Fuzzy C-Means the nature-inspired metaheuristic algorithms have also become alternate techniques for solving the codebook generation problem. Fruit Fly Optimization Algorithm (FFA) is a simple and efficient algorithm, but the capturing of an agent by a local minimum point is the main problem. Therefore, the fruit flies generally do not reach the global solution at the end of the iterations. In this study, the FFA is empowered with a smart exponential flight approach to finding out a global optimum codebook. In this approach, if a fruit fly agent is captured by a local minimum point accidentally, the smart exponential flight steps provide an opportunity to escape from it easily. In the experimental studies, successful compression results have been taken in terms of lower error rates. The numerical results prove that the proposed Smart Exponential flight-based Fruit Fly Algorithm (SE-FFA) is better than the variations of convolutional FFA by providing a global optimum codebook.

In this paper, an alternative active contour model (ACM) driven by an oppositional fruit fly algorithm (OFA) is presented. Unlike the traditional ACM variant, which is frequently caught in a local minimum, this methodology helps the focalizing of control points toward the global least of the energy function. In the proposed system, energy minimization is performed through a fruit fly algorithm, and every control point is compelled in a local search window. As for the local search window, the rectangular-shaped approach has been viewed. The results demonstrated that the fruit fly strategy utilizing polar coordinates is, for the most part, desirable over the fruit fly performed in rectangular shapes. Three performance metrics, such as the Jaccard index, the Dice index, and the Hausdorff distance, were utilized to validate the proposed strategy in real agricultural and synthetic images. From the results, it is clear that the proposed OFA technique shows a great option for the agricultural plant image segmentation process, considering any kind of disease that occurred in plant leaves.

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs).
 Keywords: Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

In this paper, an advanced fruit fly algorithm (FOA) is proposed and applied in subarray phased array antenna synthesis. The proposed algorithm introduces orthogonal crossover, quantum selection and simulated annealing operations on the individuals, and then combines them by using an adaptive expansion-contraction factor. Accordingly, a linear generation mechanism of candidate solution based fruit fly algorithm (LGMS-FOA) is generated, in which individuals are selected in a highly balanced way, and the poor solutions are still accepted with a varying probability during the iteration. These mechanisms help the proposed algorithm enhance the population diversity and global searching capability but avoid falling into local optimum. Numerical classical unimodel benchmark functions are provided to test the proposed algorithm (OLFOA) in comparison with other advanced algorithms. In addition, to further validate its superiority, the proposed algorithm is applied to handle the subarray array synthesis of several tough planar and circular apertures with different array sizes and subarray shapes. Simulation results show that the proposed OLFOA can achieve better performance than other improved evolutionary algorithms.

To eliminate the noise of infrared thermal image without reference and noise model, an improved dual-tree complex wavelet transform (DTCWT), optimized by an improved fruit-fly optimization algorithm (IFOA) and bilateral filter (BF), is proposed in this paper. Firstly, the noisy image is transformed by DTCWT, and the noise variance threshold is optimized by the IFOA, which is enhanced through a fly step range with inertia weight. Then, the denoised image will be re-processed using bilateral filter to improve the denoising performance and enhance the edge information. In the experiment, the proposed method is applied to eliminate both addictive noise and multiplicative noise, and the denoising results are compared with other representative methods, such as DTCWT, block-matching and 3D filtering (BM3D), median filter, wiener filter, wavelet decomposition filter (WDF) and bilateral filter. Moreover, the proposed method is applied as pre-processing utilization for infrared thermal images in a coal mining working face.

As an alternative to traditional evolutionary algorithms (EAs), population-based incremental learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiments results are presented that highlight the features of the new algorithms.

State space relaxation for set covering problems related to bus driver scheduling

This paper presents a new binary optimization technique for solving the 0–1 knapsack problem. This algorithm is based on converting the continuous search space of the recently proposed quadratic interpolation optimization (QIO) into discrete search space using various V-shaped and S-shaped transfer functions; this algorithm is abbreviated as BQIO. To further improve its performance, it is effectively integrated with a uniform crossover operator and a swap operator to explore the discrete binary search space more effectively. This improved variant is called BIQIO. Both BQIO and BIQIO are assessed using 20 well-known knapsack instances and compared to four recently published metaheuristic algorithms to reveal their effectiveness. The comparison among algorithms is based on three performance metrics: the mean fitness value, Friedman mean rank and computational cost. The first two metrics are used to observe the accuracy of the results, while the last metric is employed to show the efficiency of each algorithm. The results of this comparison reveal the superiority of BIQIO over the classical BQIO and four rival optimizers.

We present a Lagrangian-based heuristic for the well-known Set Covering Problem (SCP). The algorithm was initially designed for solving very large scale SCP instances, involving up to 5,000 rows and 1,000,000 columns, arising from crew scheduling in the Italian Railway Company, Ferrovie dello Stato SpA. In 1994 Ferrovie dello Stato SpA, jointly with the Italian Operational Research Society, organized a competition, called FASTER, intended to promote the development of algorithms capable of producing good solutions for these instances, since the classical approaches meet with considerable difficulties in tackling them. The main characteristics of the algorithm we propose are (1) a dynamic pricing scheme for the variables, akin to that used for solving large-scale LPs, to be coupled with subgradient optimization and greedy algorithms, and (2) the systematic use of column fixing to obtain improved solutions. Moreover, we propose a number of improvements on the standard way of defining the step-size and the ascent direction within the subgradient optimization procedure, and the scores within the greedy algorithms. Finally, an effective refining procedure is proposed. Our code won the first prize in the FASTER competition, giving the best solution value for all the proposed instances. The algorithm was also tested on the test instances from the literature: in 92 out of the 94 instances in our test bed we found, within short computing time, the optimal (or the best known) solution. Moreover, among the 18 instances for which the optimum is not known, in 6 cases our solution is better than any other solution found by previous techniques.

Symbiotic organisms search algorithm is a population-based evolutionary optimization technique that is motivated by the simulation of social behaviour that emanates from the symbiosis relationship amongst organisms in an ecosystem. It is a popular global search swarm intelligence metaheuristic that is widely being used in conjunction with several other algorithms in different fields of study. Fascinatingly, the algorithm has also been shown to have the capability of optimizing several NP-hard problems in both continuous and binary search spaces. More so, because most of the modern day real-world computational problems requires machines with high processing power and improved optimization techniques, it is important to find ways to improve the speedup of the optimization process of this algorithm, as the complexity of the problems increase. Therefore, this paper explores the possibility of improving the optimization speedup and performance of the symbiotic organisms search algorithm through parallelization methods. The proposed parallelization procedure is implemented using OpenMP on a shared memory architecture and evaluated on a set of twenty mathematical test problems. The computational results of the parallel symbiotic organisms search algorithm was compared to its serial counterpart using a measure of run-time complexity.

A fuzzy transfer function based on the behavior of meta-heuristic algorithm and its application for high-dimensional feature selection problems

Optimization at the industry level is a fundamental area since it allows reducing costs and being more sustainable. Many of these problems are combinatorial and NP-hard. On the other hand, swarm intelligence metaheuristics have been able to successfully address these types of problems, however, many of these techniques work in searching continuous spaces. In this article, we explore a general binarization mechanism of continuous metaheuristics based on the k-means technique. In particular, we applied the k-means technique to the bat algorithm with the aim of addressing the set covering problem (SCP). Experiments were designed to evaluate the contribution of the k-means technique in the binarization process. In addition, we verify the effectiveness of our algorithm through reference instances. The results indicate that the k-means binary bat algorithm (KBBA) gets adequate results when evaluated with a combinatorial problem like SCP.

From the perspective of the cost analysis of commodity marketing, the logistics cost accounts for about 30% of the final price of commodities, and the cost of storage selection accounts for about 75% of all logistics costs. This means that the management of the operation efficiency of storage is an essential part of logistics management. With four different modified fruit fly optimization algorithms, this study aimed to optimize the logistics storage selection. First, this study selected 10, 20, and 30 freight sections in a random manner; second, four fruit fly optimization algorithms were used to create some populations and fruit flies, which were taken as freight section spots; third, the algorithms were adopted to seek the logistics storage selection that costs the shortest time. According to analysis of the result, the chaos fruit fly algorithm, out of the four, was the one that took the shortest time and created the greatest effect when 10, 20, or 30 freight sections were considered. The 100 repetitious experiments also demonstrated that the chaos fruit fly algorithm cost the shortest time in the selection, and had the lowest time value variable.