Binary Cat Swarm Optimization Research Articles

A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

Approximating the pareto fronts for synthesis of planar antenna array using binary cat swarm optimization algorithm

Various performance indices like peak sidelobe level (PSLL), first null beamwidth (FNBW) and count of elements contrast to each other while designing an antenna array. A swarm-based intelligence mechanism has been discussed here for optimizing these performance indices. In particular, a pareto front is approximated for bringing out the best compromised array performance having low PSLL with narrow FNBW while maintaining a smaller number of antenna elements. A thinned planar antenna array of 20×10 was synthesized using multi-objective modified binary cat swarm optimization (MO-BCSO) algorithm to obtain the optimal positions of the array elements. The indicated technique is most appropriate for the synthesis of large antenna arrays.

Optimization of Linear Antenna Array Using Binary Cat Swarm Optimization (BCSO)

Background: Antennas serve a vital aspect in modern wireless communication. Designing antennas with very high directivity is very important to solve the long-distance communication problem. Though regularly excited and evenly spaced linear antenna arrays delivers good directivity but also leads to problem related to higher side lobe. For diminishing the level of side lobe, the array can be constructed either by amending the excitation amplitudes non-uniformly with all physical spaces of the antenna elements keeping consistent or vice versa. Methods: In this work, a novel mathematical objective function has been formulated. The objective function has been solved using a recently developed evolutionary optimization technique, i.e., Binary cat swarm optimization. So for better efficiency, the cat swarm optimization technique has been modified. Results: The results have been compared with the popular algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO) in terms of Side Lobe Level (SLL), achieved fitness and execution time. The proposed algorithm achieves 0.5dB, 1.7 dB and 3dB smaller SLL as compared to CSO, PSO and GA respectively. In addition to SLL, achieved fitness using BCSO is in the range of 0.001 which is smallest among the compared algorithms. Conclusion: It was found that the modified version namely binary cat swarm optimization algorithm outperform other well-known evolutionary optimization algorithms.

Discrete Island-Based Cuckoo Search with Highly Disruptive Polynomial Mutation and Opposition-Based Learning Strategy for Scheduling of Workflow Applications in Cloud Environments

The optimization-based scheduling algorithms used for scheduling workflows in cloud computing environments may easily get trapped in local optima, especially in the beginning of their simulation processes because of some limitations in their exploration methods. Moreover, the performance of some optimization-based scheduling algorithms may severely degrade when dealing with medium- or large-size scheduling problems. The Island-based Cuckoo Search with highly disruptive polynomial mutation (iCSPM) algorithm is a parallel version of the Cuckoo Search (CS) algorithm. The iCSPM algorithm incorporates the island model into CS and uses an exploration function based on the highly disruptive polynomial mutation. It has been empirically proven that iCSPM performs better than popular optimization algorithms (e.g., CS and island-based Genetic algorithm). This paper presents a variation of iCSPM called Discrete iCSPM with opposition-based learning strategy (DiCSPM) for scheduling workflows in cloud computing environments based on two objectives: computation and data transmission costs. DiCSPM includes two new features compared to iCSPM. First, it uses the opposition-based learning approach (OBL) in the initialization step at the level of islands, where each island in the island model contains the opposite population of another island. Second, the smallest position value method is used in the DiCSPM algorithm to determine the correct values of the decision variables in the candidate solutions. The proposed algorithm was experimentally evaluated and compared to well-known scheduling algorithms [Best Resource Selection, Particle Swarm Optimization (PSO) and Grey Wolf Optimizer] using two types of workflows: balanced and imbalanced workflows. The overall experimental and statistical results indicate that DiCSPM provides solutions for the scheduling problem of workflows in cloud computing environment faster than the other compared algorithms. Moreover, DiCSPM was evaluated and compared to state-of-the-art algorithms, namely PSO, binary PSO and discrete binary cat swarm optimization using scientific workflows of different sizes using WorkflowSim. The obtained results suggest that DiCSPM provides the best makespan compared to the other algorithms.

A binary water wave optimization for feature selection

A search method that finds a minimal subset of features (over a feature space) that yields maximum classification accuracy is proposed. This method employs rough set theory (RST) along with a newly introduced binary version of the water wave optimization approach (WWO) which is denoted by BWWO. WWO simulates the phenomena of water waves, such as propagation, refraction, and breaking and is one of the newest nature inspired methods for global optimization problems. In our approach, BWWO utilizes the phenomena of water waves propagation, refraction, and breaking in a binary version. Two main experiments based on the rough set approach and wrapper method as a part of the objective function are carried out to verify the performance of the proposed algorithm. In the first experiment, the effectiveness of the proposed approach based on RST is demonstrated on 16 different datasets. The proposed approach is compared with various typical attribute reduction methods and popular optimizers in the literature, such as ant colony, nonlinear great deluge algorithm, scatter search and others. For the second experiment, a feature subset that maximizes the classification accuracy (using cross-validated kNN classifier) with minimizing the number of selected features is obtained over 17 different datasets. In wrapper experiment BWWO is compared with the binary gray wolf optimization, binary particle swarm optimizer, binary cat swarm optimization, binary dragonfly algorithm and the binary bat algorithm. The computational results demonstrate the efficiency and effectiveness of the proposed approach in finding a minimal features subset that maximize the classification accuracy. Furthermore, Friedman test and Wilcoxon's rank-sum test are carried out at 5% significance level in this study.

Simplified binary cat swarm optimization

Inspired by the biological behavior of domestic cats, the Cat Swarm Optimization (CSO) is a metaheuristic which has been successfully applied to solve several optimization problems. For binary problems, the Boolean Binary Cat Swarm Optimization (BBCSO) presents consistent performance and differentiates itself from most of the other algorithms by not considering the agents as continuous vectors using transfer and discretization functions. In this paper, we present a simplified version of the BBCSO. This new version, named Simplified Binary CSO (SBCSO) which features a new position update rule for the tracing mode, demonstrates improved performance, and reduced computational cost when compared to previous CSO versions, including the BBCSO. Furthermore, the results of the experiments indicate that SBCSO can outperform other well-known algorithms such as the Improved Binary Fish School Search (IBFSS), the Binary Artificial Bee Colony (BABC), the Binary Genetic Algorithm (BGA), and the Modified Binary Particle Swarm Optimization (MBPSO) in several instances of the One Max, 0/1 Knapsack, Multiple 0/1 Knapsack, SubsetSum problem besides Feature Selection problems for eight datasets.

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios.

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

Selection criteria for oil transformer measurements to calculate the Health Index

In this paper, a study of the effect of a group of transformer measurements on Health Index (HI) calculation is presented. Different methodologies for selecting the most efficient group of diagnostic measurements used in classifying transformer HI are investigated. A Binary Cat Swarm Optimization (BCSO) technique is undertaken based on Support Vector Machines (SVM). The technique depends on selecting the optimal parameters for SVM. The effect of selecting HI classes as well as class's boundaries is also studied. The measurements of fourteen diagnostic transformer tests, including the furan analysis, dissolved gas analysis, and further oil analysis for 724 distribution transformers are studied, and the corresponding HI is calculated according to industrial standards. The model renders the best-selected group of measurements that assist in the formulation of the health index with minimum error and high confidence.

Analyzing the effects of binarization techniques when solving the set covering problem through swarm optimization

The Set Covering Problem (SCP) is one of the classical Karp’s 21 NP-complete problems. Although this is a traditional optimization problem, we find many papers assuming metaheuristics for solving the SCP in the current literature. However, while the SCP is a discrete problem, most metaheuristics are defined for solving continuous optimization problems, specially Swarm Intelligence Algorithms (SIAs). Hence, such algorithms should be adapted for working on the discrete scope, but most authors did not perform any study to select a concrete binarization approach. This situation might lead to the conclusion that selecting a concrete binarization technique does not influence the behavior of the algorithm, but rather the general approach of the metaheuristic. This circumstance led us to write this paper focusing on the inherent difficulty in binarization of metaheuristics designed for continuous optimization, when solving a discrete optimization problem, concretely the SCP. To this end, we consider a recent SIA inspired by the behavior of cats and adapted to the discrete scope, which is called Binary Cat Swarm Optimization (BCSO). We replace the binarization technique assumed in the original BCSO by forty different approaches from the current literature. The results obtained while solving a standard SCP benchmark are analyzed through a widely accepted statistical method, concluding that it is crucial to select an adequate binarization approach to ensure that the solving algorithm reaches its full potential. Thus, the task of adapting a metaheuristic to the discrete scope is not a simple matter and should be carefully studied. To this end and as a result of this study, we give some recommendations to perform this task.

Synthesis of Thinned Planar Antenna Array Using Multiobjective Normal Mutated Binary Cat Swarm Optimization

The process of thinned antenna array synthesis involves the optimization of a number of mutually conflicting parameters, such as peak sidelobe level, first null beam width, and number of active elements. This necessitates the development of a multiobjective optimization approach which will provide the best compromised solution based on the application at hand. In this paper, a novel multiobjective normal mutated binary cat swarm optimization (MO-NMBCSO) is developed and proposed for the synthesis of thinned planar antenna arrays. Through this method, a high degree of flexibility is introduced to the realm of thinned array design. A Pareto-optimal front containing all the probable designs is obtained in this process. Targeted solutions may be chosen from the Pareto front to satisfy the different requirements demonstrating the superiority of the proposed approach over multiobjective binary particle swarm optimization method (MO-BPSO). A comparative study is carried out to quantify the performance of the two algorithms using two performance metrics.

A Binary Cat Swarm Optimization Algorithm for the Non-Unicost Set Covering Problem

The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library.