A modified symbiotic organisms search algorithm for unmanned combat aerial vehicle route planning problem

Unmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenarios with obstacles. To solve the problem, a hybrid differential symbiotic organisms search (HDSOS) algorithm is proposed by combining the mutation strategy of differential evolution (DE) with the modified strategies of symbiotic organism search (SOS). The proposed algorithm preserves the local search capability of SOS, and at the same time has impressive global search ability. The concept of traction function is put forward and used to improve the efficiency. Moreover, a perturbation strategy is adopted to further enhance the robustness of the algorithm. Extensive simulation experiments and comparative study in two-dimensional and three-dimensional scenarios show the superiority of the proposed algorithm compared with particle swarm optimization (PSO), DE, and SOS algorithm.

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

The Journal of Supercomputing ◽

10.1007/s11227-021-03626-6 ◽

2021 ◽

Author(s):

Hekmat Mohmmadzadeh ◽

Farhad Soleimanian Gharehchopogh

Keyword(s):

Feature Selection ◽

Search Algorithm ◽

Symbiotic Organisms Search ◽

Selection Problems ◽

Symbiotic Organisms

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

Advances in Structural Engineering ◽

10.1177/13694332211026219 ◽

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).

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Symbiotic organisms search algorithm using random walk and adaptive Cauchy mutation on the feature selection of sleep staging

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114887 ◽

2021 ◽

pp. 114887

Author(s):

Fahui Miao ◽

Li Yao ◽

Xiaojie Zhao

Keyword(s):

Feature Selection ◽

Random Walk ◽

Search Algorithm ◽

Sleep Staging ◽

Symbiotic Organisms Search ◽

Cauchy Mutation ◽

Symbiotic Organisms ◽

Selection Of

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Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2021100109 ◽

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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