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Conflict Monitoring Optimization Heuristic Inspired by Brain Fear and Conflict Systems

Mojtaba Moattari , Mohammad Hassan Moradi


This paper deals with a brain-inspired approach to design evolutionary optimization algorithm. The approach is named as Conflict Monitoring Optimization inspired by two related processes in the Brain, conflict monitoring and fear processing systems. First of all, the current issues of optimization metaheuristic are discussed and challenges are addressed. Afterwards, the paper discusses a brief review of researchers’ works in danger processing (fear) system of the Brain in three different aspects. Then a model based on fear system model is derived and checked out. Next, the role of Anterior Cingulate Cortex in conflict monitoring of information is used as a seal of approval to the proposed algorithm. The finalized algorithm at last, is modified to take mutation parameters to intensify the evolutionary aspects of model. After proposing arbitrary subprograms, the proposed algorithm is checked out with 20 benchmark functions with dimension length of 3, 10 and 50. The evaluation result is compared with well-known metaheuristics for 50 different runs and its effectiveness on different function types is discussed afterwards.


Unconstrained optimization, fear system, conflict monitoring system, Amygdala, system modeling.

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