Ananya Algorithm: A Simple and New Optimization Algorithm for Engineering Optimization (2024)

FAQs

What is the Ananya algorithm? ›

The Ananya Algorithm is a new and simple optimization algorithm that involves moving towards a better solution based on the difference between the mean of variables and the best variable. It is tested on various benchmark functions and outperforms particle swarm optimization and genetic algorithm.

The simplest and most fundamental optimization algorithm is definitely the naive search. Easy to code, guaranteed global optimum, guaranteed lifelong of waiting for complex problems. The first speed-up is to use the gradient, aka first-derivatives, aka slope, to figure out in which direction your function decreases.

The most common optimization algorithm is gradient descent which updates parameters iteratively until it finds an optimal set of values for the model being optimized.

Most widely used First order optimization algorithm is Gradient Descent. The First order derivative tells us whether the function is decreasing or increasing at a particular point. First order Derivative basically give us a line which is Tangential to a point on its Error Surface.

Best first search (BFS) is a search algorithm that functions at a particular rule and uses a priority queue and heuristic search. It is ideal for computers to evaluate the appropriate and shortest path through a maze of possibilities. Suppose you get stuck in a big maze and do not know how and where to exit quickly.

Share 'Black Widow Optimization Algorithm'

A novel bio-inspired meta-heuristic optimization algorithm called Black Widow Optimization Algorithm (BWOA) is presented. BWOA mimics the spiders' different movement strategies for courtship-mating, guaranteeing the exploration and exploitation of the search space.

1. Brute Force Algorithm: This is the most basic and simplest type of algorithm.

Gradient Descent, Stochastic Gradient Descent, Mini-batch Gradient Descent, Adagrad, RMS Prop, AdaDelta, and Adam are all popular deep-learning optimizers.

Random Forest algorithm has highest accuracy test followed by SVM.

The Adam optimizer, short for “Adaptive Moment Estimation,” is an iterative optimization algorithm used to minimize the loss function during the training of neural networks. Adam can be looked at as a combination of RMSprop and Stochastic Gradient Descent with momentum.

How many optimization techniques are there? ›

– classical optimization techniques, – linear programming, – nonlinear programming, – geometric programming, – dynamic programming, – integer programming, – stochastic programming, – evolutionary algorithms, etc.

AI optimization algorithms are computational methods that aim to find the best solution among a set of possible solutions to a specific problem. These problems often involve maximizing or minimizing a certain objective, subject to a set of constraints.

2 Ant Colony Optimization. ACO, developed by Dorigo (Dorigo and Stützle, 2004), is an iterative algorithm and belongs to the class of metaheuristics.

The A* algorithm uses both the actual distance from the start and the estimated distance to the goal. Compare the algorithms: Dijkstra's Algorithm calculates the distance from the start point. Greedy Best-First Search estimates the distance to the goal point. A* is using the sum of those two distances.

Tabu search is a metaheuristic local search method used for mathematical optimization. Local search methods have the tendency to be stuck in suboptimal regions. TS enhances the performance of these techniques by prohibiting already visited solutions or others through user-provided rules.

The tabu search algorithm is used to determine the plant location. The tabu search is an optimization technique that uses a guided local search procedure that avoids local opti ma and rejects moves to points already visited in the search space by means of the so-called tabu list.