scholarly journals Chaotic Immune Symbiotic Organisms Search Algorithm for Solving Optimisation Problem

2018 ◽  
Vol 7 (3.15) ◽  
pp. 73 ◽  
Author(s):  
Mohamad Khairuzzaman Mohamad Zamani ◽  
Ismail Musirin ◽  
Saiful Izwan Suliman ◽  
Sharifah Azma Syed Mustaffa
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Optimal Solutions ◽  
Test Functions ◽  
High Quality ◽  
Symbiotic Organisms Search ◽  
Benchmark Test Functions ◽  
Symbiotic Organisms ◽  
Optimisation Techniques ◽  
Optimisation Algorithms

Achieving an optimal solution is very crucial while solving a problem. To achieve the optimality required, optimisation techniques can be implemented while solving the problem. The presence of classical optimisation techniques has enabled an optimal solution to be obtained. However, as the complexity of the optimisation problem increased, classical optimisation techniques faced difficulties in providing optimal solutions. Heuristics-based algorithms were introduced to counter the problem faced by classical optimisation techniques. Good performance of these heuristics-based algorithm has been implied through various implementation in solving optimisation problems. Despite the performance of these algorithms, the flaws of these algorithms hinder them from producing high-quality results. To mitigate the problem, this paper presents the development of Chaotic Immune Symbiotic Organisms Search algorithm which was inspired by the element of diversification as well as the increased capability of exploration. The performance of the proposed algorithm has been tested by solving several benchmark test functions. A comparative study was also conducted with respect to several other existing optimisation algorithms resulted in the superiority of the proposed algorithm in providing high-quality solutions.  

scholarly journals A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm for Ecosystem Sustainability

2021 ◽  
Author(s):  
Ajoze Abdulraheem Zubair ◽  
Shukor Bin Abd Razak ◽  
Md. Asri Bin Ngadi
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Scheduling Algorithm ◽  
Geometric Mean ◽  
Optimal Solution ◽  
Distinct Species ◽  
Scheduling Problems ◽  
Or Efficiency ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

An enhanced symbiotic organisms search algorithm for design optimization of trusses with frequency constraints

2021 ◽  
pp. 136943322110262
Author(s):  
Mohammad H Makiabadi ◽  
Mahmoud R Maheri
Keyword(s):  
Standard Deviation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Metaheuristic Algorithms ◽  
Minimum Mass ◽  
Multiple Frequency ◽  
Frequency Constraints ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms ◽  
Dynamic Frequency

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

2021 ◽  
Vol 12 (4) ◽  
pp. 169-185
Author(s):  
Saida Ishak Boushaki ◽  
Omar Bendjeghaba ◽  
Nadjet Kamel
Keyword(s):  
Clustering Algorithm ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Latent Semantic Indexing ◽  
Research Area ◽  
Semantic Indexing ◽  
Local Optima ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

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