Optimization Techniques Research Papers

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The new solutions are selected from the current populations according to their value fitness functions (Table1). Harmony search was firstly proposed in the dissertation by Geem [19], then presented in a journal paper by the Geem et al. It is derived from an artificial phenomenon found in musical performance namely the process of searching for better harmony.

Musical performances seek a best state determined by aesthetic estimation, as the optimization algorithms seek a best state determined by objective function evaluation.

Among from these elements, the aspiration criterion is used to determine the best search movie.

The new solutions are chosen from the neighborhood of the current solution and the solution which has the minimum cost becomes the new current solution [21].

Genetic algorithms based on the Darwin’s theory about evolution [1].

These algorithms start with a randomly generated initial population which is a set of possible solutions related to the problem.It is hoped that this work will be useful to researchers involved in optimization.Keywords: Optimization algorithms; Civil engineering; Review Optimization problem is defined as finding the best solution from the feasible solution in a pool which contains all solutions.Unemployed bees choose the source of food with certain probability by following the dances of the employed bees.The unemployed bees turn to the source of the selected food and begin to store nectar as employed bees.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.In order to overcome the difficulties, researchers are interested in advanced optimization techniques.In the recent literature, researchers have applied the advanced optimization techniques to different purposes.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.

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