A Framework for Implementing Metaheuristic Algorithms Using Intercellular Communication

Metaheuristics (MH) are Artificial Intelligence procedures that frequently rely on evolution. MH approximate difficult problem solutions, but are computationally costly as they explore large solution spaces. This work pursues to lay the foundations of general mappings for implementing MH using Synthetic Biology constructs in cell colonies. Two advantages of this approach are: harnessing large scale parallelism capability of cell colonies and, using existing cell processes to implement basic dynamics defined in computational versions. We propose a framework that maps MH elements to synthetic circuits in growing cell colonies to replicate MH behavior in cell colonies. Cell-cell communication mechanisms such as quorum sensing (QS), bacterial conjugation, and environmental signals map to evolution operators in MH techniques to adapt to growing colonies. As a proof-of-concept, we implemented the workflow associated to the framework: automated MH simulation generators for the gro simulator and two classes of algorithms (Simple Genetic Algorithms and Simulated Annealing) encoded as synthetic circuits. Implementation tests show that synthetic counterparts mimicking MH are automatically produced, but also that cell colony parallelism speeds up the execution in terms of generations. Furthermore, we show an example of how our framework is extended by implementing a different computational model: The Cellular Automaton.

Some Other Applications of Genetic Algorithms (GAs)

As we had already seen that genetic algorithms (GAs) are smart in their working. Here, the authors explore the rich working of genetic algorithms (GAs) in various diversified fields. Until now, they had discussed the historical nature of genetic algorithms (GAs). They have also discussed the programming code to run simple genetic algorithms (SGA). Lastly, they are going to take an overview of the application of genetic algorithms (GAs) in various fields (i.e., from business to non-business). Already, they have discussed the robust working of genetic algorithms (GAs) in various adverse conditions. Here, they discuss the application of genetic algorithms (GAs) in various other diversified fields.

An Enhanced Fractional Order Model of Ionic Polymer-Metal Composites Actuator

Ionic polymer-metal composites (IPMCs) are electroactive polymers which transform the mechanical forces into electric signals and vice versa. The paper proposes an enhanced fractional order transfer function (FOTF) model for IPMC membrane working as actuator. In particular the IPMC model has been characterized through experimentation, and a more detailed structure of its FOTF has been determined via optimization routines. The minimization error was attained comparing the simple genetic algorithms with the simplex method and considering the error between the experimental and model derived frequency responses as cost functions.

Improvement and Application of Genetic Algorithms in Reactive Power Optimization of Power Network

Reactive power optimization that is optimized by Simple Genetic Algorithms has many limitations. According to the problem of reactive power optimization of high voltage system, the Simple Genetic Algorithms is improved. The improved algorithm is applied in reactive power optimization of IEEE-6 bus system, the results indicate that the improvement is effective and it can accelerate the convergence speed and enhance the ability of optimization.