Symbiotic Organisms Search Algorithms in Optimization Problems

The Symbiotic Organisms Search (SOS) algorithm is a nature-inspired metaheuristic that mimics interactions among organisms in an ecosystem to solve complex optimisation problems. It operates through three phases—mutualism, commensalism and parasitism—each modelled on distinct biological relationships, and balances exploration of the search space with exploitation of promising regions. Since its introduction, SOS has been valued for its simplicity, few control parameters and adaptability to continuous, discrete and combinatorial domains. Researchers have extended the canonical SOS by incorporating weighting strategies, hybrid local-search operators and ensemble frameworks, improving convergence speed and solution quality. Practical applications span scheduling, facility layout, energy management, network intrusion detection and predictive modelling, demonstrating the global significance of SOS in engineering, computer science and operations research.

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In the field of network security, a clustering approach combining a Kohonen neural network with SOS has been proposed to enhance intrusion detection. The SOS component optimises neural weights, yielding higher detection rates and lower false alarms when evaluated on benchmark intrusion datasets. This hybrid model illustrates the algorithm’s versatility in tuning complex classifiers and its potential to strengthen cyber-defence systems.

A hybrid Smell Agent Symbiosis Organism Search (SASOS) algorithm has been developed for autonomous microgrid control. By integrating SOS with a smell-agent optimisation routine, the method achieves a more effective balance between exploration and exploitation, resulting in improved frequency and voltage regulation. Simulations using standard test functions and microgrid disturbance scenarios confirm that SASOS outperforms both its parent algorithms in convergence speed and harmonic distortion reduction.

To address limitations of over-exploration in the standard SOS, a dynamic weighted SOS variant has been introduced. This version employs adaptive and random weight strategies to generate modified interaction vectors during mutualism and parasitism phases. Extensive benchmarking on mathematical test functions and real-world problems demonstrates that the weighted SOS delivers more reliable convergence and superior solution quality compared with the original algorithm and several other metaheuristics.

Symbiotic Organisms Search Algorithms in Optimization Problems publication trend

The graph below shows the total number of articles in symbiotic organisms search algorithms in optimization problems across all publications each year (not limited to Nature Index journals).

Technical terms

Symbiotic Organisms Search (SOS) algorithm: A bio-inspired metaheuristic that emulates mutual interactions among organisms to explore and exploit a search space for optimisation.

Metaheuristic optimisation: A high-level problem-solving framework that combines heuristic strategies to find near-optimal solutions for complex, non-linear problems.

Exploration: The process of broadly sampling the search space to identify diverse candidate solutions.

Exploitation: The focused refinement of promising solutions to improve convergence toward an optimum.

Mutualism phase: An interaction in SOS where two candidate solutions share information for mutual improvement.

Commensalism phase: A phase in which one solution benefits from another without adversely affecting its partner.

Parasitism phase: A competitive interaction where a new candidate (parasite) replaces a weaker existing solution if it proves fitter.

References

1. An ensemble symbiosis organisms search algorithm and its application to real world problems. Decision Science Letters (2018).
2. Kohonen neural network and symbiotic-organism search algorithm for intrusion detection of network viruses. Frontiers in Computational Neuroscience (2023).
3. A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations. PLOS ONE (2023).
4. Dynamic Weighted Symbiotic Organisms Search Algorithm for Global Optimization Problems. Complexity (2023).