Abstract
This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.
Highlights
Optimization is a tedious iterative process based on a comprehensive search among the trial solution alternatives to obtain the optimum solution for a particular problem
Numerical experiments based on unconstrained multimodal and unimodal benchmark functions indicate that incorporating the chaotic maps into the the base metaheuristic algorithm have considerably improved the solution quality and accuracy
It is shown that Kent map (CM05), Chirikov map (CM03), and Standard map (CM09) based chaotic Manta Ray Foraging Optimization (MRFO) algorithms provide the most reliable statistical results compared to the remaining chaotic methods
Summary
Optimization is a tedious iterative process based on a comprehensive search among the trial solution alternatives to obtain the optimum solution for a particular problem. Algorithms belonging to deterministic optimization methods are frequently used when locating the global best answer to the problem is a necessity or in extreme cases when it is very time-consuming and exhaustive to find a feasible solution These types of algorithms can be unproductive and become useless in finding the exact solutions to NP (Non Polynomial)-hard multidimensional problems. Such algorithms can provide very efficient results without any guarantee of finding the global optimum solution. They do not impose preconditions to the solution domain such as differentiability and continuity which eases their successful applications to various design problems [3]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
