Chaotic Symbiotic Organisms Search Research Articles

A novel chaotic symbiotic organisms search optimization in multilevel image segmentation

Multilevel thresholding-based image segmentation plays a vital role in image processing. It significantly impacts many applications, such as remote sensing, pattern recognition, and medical image diagnosis. Premature convergence due to stuck into the local optima is the main challenge of any evolutionary algorithm-based multilevel image thresholding. Most of the evolutionary algorithms use their stochastic property to comprehensively utilize the search space, which strongly influences premature convergence. This paper presents a novel chaotic symbiotic organisms search (CSOS) optimization for multilevel image segmentation that maintains a strategic distance from premature convergence and improves the performance of conventional symbiotic organisms search (SOS) optimization in multilevel image segmentation. We have analyzed the performance of the proposed CSOS using state-of-the-art entropies such as Kapur’s, Tsallis’, Renyi’s, and Masi’s entropy as objective functions. The experiments on standard used color images are presented to establish the practicality of the proposed algorithm. The results show that the CSOS algorithm with Masi’s entropy is more effective and has wide adaptability to the high-dimensional optimization problems than the other recently proposed algorithms considered in this paper.

An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems

Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms Search (SOS) is one of the most successful meta-heuristic algorithms inspired by organisms' interaction in nature called mutualism, commensalism, and parasitism. In this paper, three SOS-based binary approaches are offered to solve the feature selection problem. In the first and second approaches, several S-shaped transfer functions and several Chaotic Tent Function-based V-shaped transfer functions called BSOSST and BSOSVT are used to make the binary SOS (BSOS). In the third approach, an advanced BSOS based on changing SOS and the chaotic Tent function operators called EBCSOS is provided. The EBCSOS algorithm uses the chaotic Tent function and the Gaussian mutation to increase usefulness and exploration. Moreover, two new operators, i.e., BMPT and BCPT, are suggested to make the commensalism and mutualism stage binary based on a chaotic function to solve the feature selection problem. Finally, the proposed BSOSST and BSOSVT methods and the advanced version of EBCSOS were implemented on 25 datasets than the basic algorithm's binary meta-heuristic algorithms. Various experiments demonstrated that the proposed EBCSOS algorithm outperformed other methods in terms of several features and accuracy. To further confirm the proposed EBCSOS algorithm, the problem of detecting spam E-mails was applied, with the results of this experiment indicating that the proposed EBCSOS algorithm significantly improved the accuracy and speed of all categories in detecting spam E-mails.

A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for optimal allocation of DG in radial distribution networks

This paper aims to apply an improved meta-heuristic method to optimize the allocation of distributed generation (DG) units in radial distribution networks (RDNs). The proposed method, namely the Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS) algorithm, is the improved version of the original SOS algorithm. QOCSOS incorporates the Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies into SOS to improve the global search capacity. In this study, the objective of the optimal DG allocation (OGDA) problem is to optimally reduce the real power loss, improve the voltage profile, and increase the voltage stability in RDNs. The proposed QOCSOS algorithm was applied to find the optimal locations and sizes of DG units with different DG power factors (unity and non-unity) in the RDNs including 33, 69, and 118-bus. It was found that the operation of DG units with optimal power factor significantly improved the performance of RDNs in terms of voltage deviation minimization, and voltage stability maximization, especially for power loss reduction. After the DG integration, for the case of DG units operating with unity power factor, the power loss reduction was reduced by 65.50%, 69.14%, and 60.23% for the 33, 69, and 118-bus RDNs, respectively. In addition, it should be emphasized that for the cases of DG units operating with optimal power factor, the power loss reduction was reduced up to 94.44%, 98.10%, and 90.28% for these RDNs, respectively. The obtained results from QOCSOS were evaluated by comparing to those from SOS and other optimization methods in the literature. The results showed that the proposed QOCSOS method performed greater than SOS, and offered better quality solutions than many other compared methods, suggesting the feasibility of QOCSOS in solving the ODGA problem, especially for a complex and large-scale system.

An improved meta-heuristic method to maximize the penetration of distributed generation in radial distribution networks

This paper proposes a novel scheme based on an improved meta-heuristic method to determine the optimal number of distributed generation (DG) units to be installed in distribution networks for maximum DG penetration. The proposed meta-heuristic method is the quasi-oppositional chaotic symbiotic organisms search (QOCSOS) algorithm, which is the improved version of the original SOS algorithm. QOCSOS integrates two search strategies including quasi-opposition-based learning and chaotic local search into SOS to achieve better performance. In this study, QOCSOS was implemented to find the optimal number, location, size, and power factor of DG units considering different values of DG power factor (unity and non-unity), with the objective of maximum real power loss reduction. The effectiveness of the proposed method was validated on the standard IEEE radial distribution networks including 33, 69, and 118-bus test networks. The results obtained by QOCSOS were compared to those from other methods available in the literature and the standard SOS algorithm. Comparative results revealed that QOCSOS obtained better solutions than other compared methods, and performed greater than SOS. Accordingly, QOCSOS can be a very favourable method to cope with the optimal DG placement problem.

The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems

DOI : 10.26650/electrica.2019.18008 This paper presents an adaptive chaotic symbiotic organisms search algorithm (A-CSOS) for finding the solution of optimal reactive power dispatch (ORPD) problem which is one of the main issues of power system planning and operations. The most important advantage of symbiotic organisms search algorithm (SOS) is that is not need any particular algorithm parameters. However, the SOS algorithm has some features to be enhanced, like falling into local minima and sluggish convergence. A-CSOS algorithm with adding new and improved features like adaptivity and chaos to conventional SOS algorithm is proposed to solve ORPD problem. The ORPD problem is mainly focused on minimization of transmission loss (Ploss) and total voltage deviation (TVD). To determine the optimal set points of control variables including generator bus voltages, tap positions of transformers, and reactive power outputs of shunt VAR compensators is very crucial for minimization to Ploss and TVD. The proposed algorithm is implemented on IEEE 30-bus test power systems for ascertaining the performance of A-CSOS algorithm on ORPD problem. The results showed that the proposed approach is up to 10.39% better than many of which the latest algorithms in literature and encourage the researchers to implement A-CSOS algorithm to ORPD problem. Cite this article as: Yalcin E, Taplamacioglu MC, Cam E. “The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems”, Electrica, 2019; 19(1): 37-47.

An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment

In Cloud Computing model, users are charged according to the usage of resources and desired Quality of Service (QoS). Multi-objective task scheduling problem based on desired QoS is an NP-Complete problem. Due to the NP-Complete nature of task scheduling problems and huge search space presented by large scale problem instances, many of the existing solution algorithms cannot effectively obtain global optimum solutions. In this paper, a chaotic symbiotic organisms search (CMSOS) algorithm is proposed to solve multi-objective large scale task scheduling optimization problem on IaaS cloud computing environment. Chaotic optimization strategy is employed to generate initial ecosystem (population), and random sequence based components of the phases of SOS are replaced with chaotic sequence to ensure diversity among organisms for global convergence. In addition, chaotic local search strategy is applied to Pareto Fronts generated by SOS algorithms to avoid entrapment in local optima. The performance of the proposed CMSOS algorithm is evaluated on CloudSim simulator toolkit, using both standard workload traces and synthesized workloads for larger problem instances of up to 5000. Moreover, the performance of the proposed CMSOS algorithm was found to be competitive with the existing with the existing multi-objective task scheduling optimization algorithms. The CMSOS algorithm obtained significant improved optimal trade-offs between execution time (makespan) and financial cost (cost) with no computational overhead. Therefore, the proposed algorithms have potentials to improve the performance of QoS delivery.

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimal<scp>DG</scp>allocation problem in radial distribution system

A Quasi-Oppositional-Chaotic Symbiotic Organisms Search algorithm for global optimization problems

This study proposes an improved version of the Symbiotic Organisms Search (SOS) algorithm called Quasi-Oppositional Chaotic Symbiotic Organisms Search (QOCSOS). This improved algorithm integrated Quasi-Opposition-Based Learning (QOBL) and Chaotic Local Search (CLS) strategies with SOS for a better quality solution and faster convergence. To demonstrate and validate the new algorithm’s effectiveness, the authors tested QOCSOS with twenty-six mathematical benchmark functions of different types and dimensions. In addition, QOCSOS optimized placements for distributed generation (DG) units in radial distribution networks and solved five structural design optimization problems, as practical optimization problems challenges. Comparative results showed that QOCSOS provided more accurate solutions than SOS and other methods, suggesting viability in dealing with global optimization problems.

A novel chaos-integrated symbiotic organisms search algorithm for global optimization

Symbiotic organisms search (SOS) algorithm imitates the symbiotic relationship between different biological species. Simulation procedure of this algorithm is in three different phases, viz. mutualism, commensalism and parasitism. In this paper, the basic SOS algorithm is reduced and a chaotic local search is integrated into the reduced SOS to form chaotic SOS (CSOS) for improving the solution accuracy and convergence mobility of the basic SOS algorithm. The proposed CSOS algorithm is implemented and tested, successfully, on twenty-six unconstrained benchmark test functions. Experimental results presented in this paper are compared to those offered by the basic SOS. Additionally, the proposed algorithm is utilized to solve a real-world power system problem (siting and sizing problem of distributed generators in radial distribution system). The results presented in this paper show that the proposed CSOS algorithm yields superior solution over the other popular techniques in terms of convergence characteristics and global search ability for both benchmark function optimization and power engineering optimization task.

Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm

Distributed generators (DGs) play an important role to reduce the real power loss and to improve the voltage stability of the power system. However, proper placement and sizing of DGs in the distribution network is a vital issue, as any improper location and size of DGs may increase the overall system loss. In this study, a novel chaotic symbiotic organisms search (CSOS) algorithm is proposed to find out the optimal location and sizes of real power DGs in a radial distribution system (RDS) considering constant load models. This DG siting and sizing problem is associated with real power loss minimisation and voltage stability improvement objectives. The effectiveness of the proposed algorithm is tested on benchmark test functions as well as on 33-, 69- and 118-bus RDS. The simulation results obtained by the proposed CSOS algorithm are compared with the results offered by the basic SOS and other nature inspired algorithms to establish the superiority of the proposed CSOS algorithm over the others in terms of quality of solution and convergence mobility.