Swallow Swarm Optimization Research Articles

Design of optimal low-pass filter by a new Levy swallow swarm algorithm

The swallow swarm optimization (SS) is a challenging method of optimization, which has a quicker convergence speed, not getting caught in the local extreme points. However, the SS suffers from a few shortcomings—(1) the movement speed of particles is not controlled suitably during the search due to the requirement of an inertia weight and (2) the less flexibility of variables does not permit to maintain a balance between the local and the global searches. To solve these problems, a new Levy swallow swarm optimization (SSLY) algorithm with the exploitation capability is proposed. This article also provides an optimal design methodology for the low-pass filter using the suggested SSLY technique. A new objective function is introduced to achieve the maximally flat frequency response, which is another important contribution to the field. The firefly algorithm (FA), the sine cosine algorithm (SCA) and the standard global optimizers—real coded genetic algorithm (GA), conventional particle swarm optimization (PSO), cuckoo search (CS) and SS, are considered for a comparison. The proposed SSLY outperforms the FA, SCA, GA, PSO, CS and SS algorithms. Results authenticate suitability of the proposed algorithm for solving the filter design problems in the FIR domain.

DRDS-CSSO: Division and Replication of Data Security via the Crossover based Swallow Swarm Optimization in Cloud

DRDS-CSSO: Division and Replication of Data Security via the Crossover based Swallow Swarm Optimization in Cloud

Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.

Solving the Quadratic Assignment Problem using the Swallow Swarm Optimization Problem

In recent years, there is a growing interest in swarm intelligent algorithms inspired by the observation of the natural behavior of swarm to define a computational method, which may resolve the hardest combinatorial optimization problems. The Quadratic Assignment Problem is one of the well-known combinatorial problems, which simulate with the assignment problem in several domains such as the industrial domain. This paper proposes an adaptation of a recent algorithm called the swallow swarm optimization to solve the Quadratic Assignment Problem; this algorithm is characterized by a hierarchy of search who allow it to search in a totality of research space. The obtained results in solving some benchmark instances from QAPLIB are compared with those obtained from other know metaheuristics in other to evaluate the performance of the proposed adaptation.

Automatic speaker recognition with enhanced swallow swarm optimization and ensemble classification model from speech signals

An Automatic Speaker Recognition (ASR) system which applies voice as a discriminatory feature is used for authentication and verification of identity of a person. A system which is able to deliver acceptable performance in an environment with multiple operating parameters is the requirement of a robust speaker recognition system. Enhancing the performance characteristics of the ASR becomes major significant and challenging issue. The key objective of this work is to introduce a new automatic speaker recognition system for speech signals. In this proposed work, preprocessing is done with the LMS adaptive filter. Features like zero-crossing rate, energy, auto-correlation function, Mel frequency cepstral coefficient features are extracted from noise removed signals to obtain better results. Using the extracted features, input samples are created and the dimensions have been reduced using enhanced swallow swarm optimization. Finally, ensemble classification model using many classification methods such as long short-term memory, improved convolutional neural network and support vector machine is used to perform speaker recognition. The proposed method has more potential to classify speech signals in comparisons with other models. The method indicates an accuracy of 95.69%. Proposed work adopts the external quality metrics accuracy, sensitivity and specificity to compare with the existing methods.

NEW SWARM INTELLIGENCE BASED OPTIMIZATION ALGORITHMS FOR THE OPTIMIZATION OF MICROGRIDS

The need for new energy sources has increased due to reasons such as the development of technology, the increase in electricity demand, the decrease of fossil resources, and environmental pollution. Renewable energy sources are self-renewing, friendly, and clean energy sources. Microgrids are small power energy networks consisting of renewable and non-renewable energy sources, batteries, inverters, and loads. They can be operated connected to the network and independently from the network. Metaheuristic methods are algorithms that can achieve optimum results in the search space. In this study, optimization of a microgrid composed of a wind turbine, solar panel, diesel generator, inverter, and loads has been investigated with multi-objective hybrid metaheuristic algorithms. Optimization is aimed at reducing emissions, increasing reliability, and optimizing energy resources. Swallow Swarm Optimization (SSO) and Hybrid Particle Swallow Swarm Optimization (HPSSO) with different iterations and populations are compared for the first time.

An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques

The conventional two dimensional (2-D) histogram based Otsu’s method gives unreliable results while considering multilevel thresholding of brain magnetic resonance (MR) images, because the edges of the brain regions are not preserved due to the local averaging process involved. Moreover, some of the useful pixels present inside the off-diagonal regions are ignored in the calculation. This article presents an evolutionary gray gradient algorithm (EGGA) for optimal multilevel thresholding of brain MR images. In this paper, more edge information is preserved by computing 2-D histogram based gray gradient. The key to our success is the use of the gray gradient information between the pixel values and the pixel average values to minimize the information loss. In addition, the speed improvement is achieved. Theoretical formulations are derived for computing the maximum between class variance from the 2-D histogram of the brain image. A first-hand fitness function is suggested for the EGGA. A novel adaptive swallow swarm optimization (ASSO) algorithm is introduced to optimize the fitness function. The performance of ASSO is validated using twenty three standard Benchmark test functions. The performance of ASSO is better than swallow swarm optimization (SSO). The optimum threshold value is obtained by maximizing the between class variance using ASSO. Our method is tested using the standard axial T2 − weighted brain MRI database of Harvard medical education using 100 slices. Performance of our method is compared to the Otsu’s method based on the one dimensional (1-D) and the 2-D histogram. The results are also compared among four different soft computing techniques. It is observed that results obtained using our method is better than the other methods, both qualitatively and quantitatively. Benefits of our method are – (i) the EGGA exhibits better objective function values; (ii) the EGGA provides us significantly improved results; and (iii) more computational speed is achieved.

A new hybrid optimization method inspired from swarm intelligence: Fuzzy adaptive swallow swarm optimization algorithm (FASSO)

In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO) method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO) method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.

Hybrid PSO and SSO algorithm for truss layout and size optimization considering dynamic constraints

A hybrid approach of Particle Swarm Optimization (PSO) and Swallow Swarm Optimization algorithm (SSO) namely Hybrid Particle Swallow Swarm Optimization algorithm (HPSSO), is presented as a new variant of PSO algorithm for the highly nonlinear dynamic truss shape and size optimization with multiple natural frequency constraints. Experimentally validation of HPSSO on four benchmark trusses results in high performance in comparison to PSO variants and to those of different optimization techniques. The simulation results clearly show a good balance between global and local exploration abilities and consequently results in good optimum solution.

An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm

In this article, search mechanisms of Swallow Swarm Optimization (SSO) are implemented in the framework of Particle Swarm Optimization (PSO) to form the Hybrid Particle Swallow Swarm Optimization (HPSSO) algorithm. The new algorithm is tested by solving eleven mathematical optimization problems and six truss weight minimization problems. HPSSO is compared to the standard PSO and some of its advanced variants. Optimization results demonstrate the efficiency of the proposed algorithm that outperforms the PSO variants taken as basis of comparison and is very competitive with other state-of-the-art metaheuristic optimization methods. Here, a good balance between global and local searches is achieved.

Swallow swarm optimization algorithm: a new method to optimization

This paper presents an exposition of a new method of swarm intelligence–based algorithm for optimization. Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method. There are three kinds of particles in this method: explorer particles, aimless particles, and leader particles. Each particle has a personal feature but all of them have a central colony of flying. Each particle exhibits an intelligent behavior and, perpetually, explores its surroundings with an adaptive radius. The situations of neighbor particles, local leader, and public leader are considered, and a move is made then. Swallow swarm optimization algorithm has proved high efficiency, such as fast move in flat areas (areas that there is no hope to find food and, derivation is equal to zero), not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles. SSO algorithm has been tested by 19 benchmark functions. It achieved good results in multimodal, rotated and shifted functions. Results of this method have been compared to standard PSO, FSO algorithm, and ten different kinds of PSO.