Optimization Techniques Research Papers

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These algorithms are; Genetic Algorithms (GA), Harmony search (HS), Artificial Bee Colony (ABC), Tabu Search (TS), Teaching– Learning-Based Optimization (TLBO), Particle Swarm Optimization (PSO), Big bang – big crunch (BBBC), Charged System Search (CSS), Cuckoo Search Algorithm (CSA), Ant Colony Optimization (ACO), Jaya, Firefly algorithm (FA), Simulated Annealing (SA), Cultural Algorithm (CA), Differential Evolution (DE), League championship algorithm (LCA), Backtracking Search Algorithm (BSA), Glowworm Swarm Optimization (GSO), Memetic Algorithm (MA), Greedy Randomized Adaptive Search Procedure (GRASP), etc.

In addition to these algorithms, similar algorithms derived from these algorithms have been developed by the researchers such as elitist TLBO and intelligent GA.

Also, the optimization problems can be classified as size, shape, and topology, discrete, continuous, single or multi-objective optimization.

The application of optimization to real word engineering problems is quite recent, mainly due to the complexity of mathematical models, described by non-linear functions and generating a non-convex space of solutions.

Employed bees, unemployed bees, and scout bees are the type of bee defined in this algorithm.

Employed bees search food around the food source and they store the nectar.Employed bees who consume food sources become scout bees to search for new sources [20].Tabu Search algorithm explores the search space by a sequence of movies.With the advent of advanced optimization methods, last decades have witnessed a growing application of optimization to a wide range of engineering problems, from automotive to biomedicine, and of course, to civil engineering.Applications of optimization techniques are most exciting, challenging, and of truly large scale when it comes to the problems of civil engineering in terms of both quality and quantity.To escape the local optimum, the certain movies are listed in a memory called forbidden (tabu) search.This algorithm contains some elements: tabu list, neighborhood, aspiration criterion, termination criterion and cost function.In each generation of the optimization process, the biological operators are used to create next population by the hope that the new population will be better the old one.The main operators used in this algorithm are selection, encoding, crossover and mutations.In the multi-objective optimization problem, the name of the existing optimization algorithm may be changed as NDS-GA (non-dominated sorting genetic algorithm).In the Table 1, the optimization algorithm and their first original papers are given.


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