Optimization Techniques Research Papers

Optimization Techniques Research Papers-56
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.

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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.

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 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.

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.

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.

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