Название: Meta-heuristic Optimization Techniques: Applications in Engineering
Автор: Anuj Kumar, Sangeeta Pant, Mangey Ram
Издательство: De Gruyter
Год: 2022
Страниц: 204
Язык: английский
Формат: pdf (true), epub
Размер: 14.1 MB

This book is motivated by the fact that meta-heuristic optimization techniques have become very popular among researchers and engineers over the last two decades. The widespread applicability of various optimization methods makes them a hot spot for researchers. A few years back one even can’t think that school of fish, genes, nature of bat or ant can be used to design optimization algorithms, but nature has the solution of every problem. Nature-inspired optimization algorithms usually attempt to find a good approximation to the solution of complex optimization problems of various fields of sciences, engineering, and industries. It is applicable in almost all spheres of human life for the purpose of optimization of various parameters.

Whereas a heuristic algorithm discovers the optimal solution in the search space of an optimization problem by “trial and error” with a weak guarantee of success, a metaheuristic algorithm performs better than that. The later uses a trade-off between randomization and local search and can be used for seeking global optima too. In metaheuristic algorithms, there is an exhaustive global exploration in the search space, and based on some current good solution, an intense search in a local region is done further to get the best solutions. Presenting an overview of some nature-inspired metaheuristic techniques has gained popularity due to their robustness and flexibility of application in different branches of engineering.

Ant colony optimization (ACO): Proposed by Dorigo, the source of inspiration for ant colony optimization (ACO) algorithmant colony optimization was the foraging nature of some ant species. The natural community behavior of these species to discover the shortest path from their nest to food helps them to survive easily. Though this behavior is purely mutual, a single ant cannot succeed in finding the shortest path [3]. An ant first randomly explores the surrounding area of her nest, locates the food source, and carries an amount of food back to its nest. During the return journey, a chemical pheromone gets deposited on the ground which indirectly guides other members to the food source. Apart from the trail to food source, the deposited pheromone also reflects the quantity and quality of food. After some time, the ants tend to follow the trail of high-quality pheromone which eventually becomes the trail of high accumulation of pheromone too. Since ants prefer to follow trails with larger amounts of pheromone, eventually all ants converge to the shortest path between their nest and food source.

Particle swarm optimization (PSO): Proposed by R. Eberhart and J. Kennedy, the population-based search algorithm particle swarm optimization (PSO) mimics the behavior of species where there is no permanent position of the leader in group. Only temporary leaders lead the group for small intervals of time during the search of food. This temporary leader is the one among all lies at the closest vicinity of the food source. For instance, the school of fish and flock of birds belong to this category. This behavior optimizes their quest of food after the finite shifts at the temporary leader position.

Cuckoo search algorithm (CSA): Developed by Xin-She Yang and Suash Deb, cuckoo search algorithm (CSA) is inspired by obligate brood parasitism of a certain species of birds. A particular species of birds like ani and guira cuckoos are very aggressive in reproduction and follow a parasitic strategy to lay their eggs in the nests of other host birds. If somehow the host bird realizes that the eggs do not belong to it, then either it removes the eggs or leaves the nest permanently. Studies show that a cuckoo bird mimics the eggs of host bird in color and shape which eventually reduces the chances of getting abandoned by the host bird. Not only this, the newly born cuckoo chicks also mimic the call of host chicks to deceive the mother host for feeding.

Contents:
Preface
Acknowledgments
Nature-inspired metaheuristic algorithms for optimization
An optimization approach for highway alignment using metaheuristic algorithms
A method for solving bi-objective transportation problem under fuzzy environment
Application of particle swarm optimization technique in an interval-valued EPQ model
Optimization techniques used for designing economic electrical power distribution
Meta-heuristic optimization techniques in navigation constellation design
Correlation and heuristic analysis of polymer-modified concrete subjected to alternate wetting and drying
q-Rung orthopair fuzzy entropy measure and its application in multi-attribute decision-making
A fuzzy multi-criteria decision-making approach for crime linkage utilizing resemblance function under hesitant fuzzy environment
Integrating novel-modified TOPSIS with central composite design to model and optimize O2 delignification process in pulp and paper industry
Deep learning for satellite-based data analysis
Editors’ Biography
Index