concept

Whale Optimization Algorithm (WOA)

Also known as: WOA, Whale Optimization Algorithm (WOA), whale optimization algorithm

Facts (47)

Sources

Comprehensive framework for smart residential demand side ... nature.com Nature Mar 22, 2025 42 facts

procedureThe Whale Optimization Algorithm (WOA) adopts a random search strategy when the vector |A| is greater than 1, and a best-position search strategy when the vector |A| is less than 1.

referenceThe mathematical model of the conventional Whale Optimization Algorithm (WOA) is based on the dynamics of the spiral bubble-net feeding maneuver and the search for prey.

procedureThe Whale Optimization Algorithm (WOA) search process is structured into three major steps: encircling prey, the bubble-net attacking method (exploitation phase), and a search phase (implied by the context of metaheuristic optimization).

procedureIn the 'Spiral updating' approach of the Whale Optimization Algorithm (WOA), the movement of the search agent toward the prey is modeled using a logarithmic spiral, which is defined by the distance between the prey and the whale, a constant 'b' that determines the shape of the spiral, and a random number 'l' in the range [-1, 1].

formulaIn the Whale Optimization Algorithm, the distance of the prey to the ith whale is calculated as D = |X*(t) - X(t)|, where X*(t) represents the position vector of the best solution and X(t) represents the position vector of the whale at iteration t.

referenceMirjalili, S. and Lewis, A. published 'The Whale optimization algorithm' in Advances in Engineering Software, volume 95, pages 51–67, in 2016.

procedureThe termination criteria for the Whale Optimization Algorithm in this study are defined by either the maximum iteration count or the saturation of consecutive iterations, whichever occurs first.

procedureThe Whale Optimization Algorithm (WOA) uses maximum iteration count and saturation for consecutive iterations as termination criteria, whichever occurs first.

procedureDuring the exploitation phase of the Whale Optimization Algorithm, a probabilistic approach is used to choose between the 'Spiral updating' model and the 'Shrinking encircling' model, determined by a random number 'p' within the interval [0, 1].

referenceThe research paper 'The Whale optimization algorithm' was published in Adv. Eng. Softw. 95, 51–67 in 2016.

procedureDuring the exploitation phase of the Whale Optimization Algorithm (WOA), the algorithm uses a probabilistic approach, utilizing a random number 'p' between 0 and 1, to select between the 'Shrinking encircling' model and the 'Spiral updating' model.

procedureThe Whale Optimization Algorithm (WOA) determines whether to follow a spiral or circular movement based on the values of the parameter P.

claimThe 'Bubble-net attacking method' (exploitation phase) of the Whale Optimization Algorithm utilizes two approaches: 'Shrinking encircling' and 'Spiral updating'.

procedureIn the Whale Optimization Algorithm exploration phase, a search agent's position is updated based on a randomly chosen search agent rather than the best search agent, which facilitates global search.

claimThe Whale Optimization Algorithm (WOA) exploration phase utilizes a search strategy based on variations of the vector A, where humpback whales search for prey randomly based on their position and distance from other whales.

procedureThe 'Bubble-net attacking method' (exploitation phase) of the Whale Optimization Algorithm employs two approaches: (1) 'Shrinking encircling,' where the step size reduces as iterations progress by decreasing the parameter 'a' from 2 to 0, which decreases the coefficient vector 'A' within the interval [-a, a]; and (2) 'Spiral updating,' which models the spiral movement of a whale towards prey using the distance between the prey and the whale, a constant 'b' defining the shape of the logarithmic spiral, and a random number 'l' in the range [-1, 1]. A probabilistic approach is used to select between these two models during the exploitation phase.

formulaIn the 'Spiral updating' approach of the Whale Optimization Algorithm, the spiral movement of the whale towards the prey is formulated using the distance D between the prey and the whale, a constant 'b' defining the shape of the logarithmic spiral, and a random number 'l' in the interval [-1, 1].

measurementThe Whale Optimization Algorithm (WOA) parameter 'a' decreases linearly from 2 to 0 to regulate the movement step size during both the exploitation and exploration phases.

referenceThe mathematical model of the conventional Whale Optimization Algorithm (WOA) is based on the dynamics of the spiral bubble-net feeding maneuver and the search for prey.

claimIn the Whale Optimization Algorithm, the parameter 'a' decreases linearly from 2 to 0 to manage the movement step size across both exploitation and exploration phases.

referenceThe discrete/binary version of the Whale Optimization Algorithm (WOA) approach uses the variable d_i^k(t) to denote the distance of a particle, and x_i^k(t) and x_i^k(t+1) to denote the current position of the ith particle at specific iterations and dimensions.

procedureIn the 'Shrinking encircling' approach of the Whale Optimization Algorithm, the step size reduces as iterations progress or as searching agents move closer to optimal values. This is implemented by decreasing the vector 'a' from 2 to 0 over the total number of iterations, which in turn decreases the coefficient vector 'A' within the interval [-a, a].

procedureThe Whale Optimization Algorithm (WOA) employs a 'Bubble-net attacking method' as its exploitation phase, which incorporates two distinct approaches: 'Shrinking encircling' and 'Spiral updating'.

procedureIn the Whale Optimization Algorithm (WOA), the exploration phase updates a search agent's position based on a randomly selected search agent, whereas the exploitation phase updates the position based on the best search agent found so far.

referenceThe Whale Optimization Algorithm (WOA) is a metaheuristic optimization technique that simulates the hunting behavior of humpback whales, specifically utilizing encircling prey and bubble-net attacking strategies.

procedureThe Whale Optimization Algorithm (WOA) adopts a random search strategy when the absolute value of vector A is greater than 1, and adopts a best position strategy when the absolute value of vector A is less than 1.

referenceThe Whale Optimization Algorithm (WOA) mathematical model is based on the dynamics of the spiral bubble-net feeding maneuver and the search for prey.

claimThe Whale Optimization Algorithm starts with a random population, and its search strategy in both exploitation and exploration stages relies on either a randomly chosen search agent or the best solution vector obtained so far.

claimThe Whale Optimization Algorithm determines whether to follow a spiral or circular movement based on the values of parameter P.

claimThe Whale Optimization Algorithm adopts a random search strategy if the absolute value of vector A is greater than 1, and a best position strategy if the absolute value of vector A is less than 1.

procedureThe Whale Optimization Algorithm (WOA) search process begins with an 'Encircling prey' step, where a population matrix is formulated based on population size and variable constraints, and search agents update their positions to move toward the best candidate solution identified by fitness value evaluation.

procedureThe 'Encircling prey' step of the Whale Optimization Algorithm involves the following process: (1) formulate the population matrix by defining population size and the number of variables within the maximum and minimum values of each variable; (2) evaluate candidate solutions based on fitness values obtained from objective functions; (3) allow search agents to move towards the best search agent identified in the current iteration; (4) update the position vector if better solutions are obtained by comparing fitness values.

procedureThe exploration phase of the Whale Optimization Algorithm (WOA) involves search agents moving randomly based on a reference whale, a process triggered when the vector |A| is greater than 1 or less than -1.

claimDuring the exploration phase of the Whale Optimization Algorithm, a search agent's position is updated according to a randomly decided search agent rather than the best search agent.

procedureIn the 'Encircling prey' step of the Whale Optimization Algorithm, the population matrix is formulated by defining population size and the number of variables within maximum and minimum bounds. Search agents update their positions toward the best candidate solution based on fitness values obtained from objective functions.

procedureThe Whale Optimization Algorithm (WOA) exploration phase utilizes a search strategy where humpback whales search for prey randomly based on their position and distance from other whales, regulated by a vector A where the absolute value of A is greater than 1.

claimIn the Whale Optimization Algorithm exploration phase, search agent movements are regulated far from a reference whale when the random values of vector A are greater than 1 or less than -1.

referenceThe discrete/binary version of the Whale Optimization Algorithm (WOA) uses the variable d_i^k(t) to denote the distance of a particle, while x_i^k(t) and x_i^k(t+1) denote the current position of the ith particle at specific iterations and dimensions.

formulaThe discrete/binary version of the Whale Optimization Algorithm (WOA) defines the distance of a particle as d_i^k(t), while x_i^k(t) and x_i^k(t+1) represent the current position of the ith particle at specific iterations and dimensions.

procedureThe Whale Optimization Algorithm (WOA) utilizes a parameter 'a' that decreases linearly from 2 to 0 to regulate the movement step size during both the exploitation and exploration phases.

procedureIn the 'Shrinking encircling' approach of the Whale Optimization Algorithm (WOA), the step size is reduced as iterations progress by decreasing the coefficient vector 'a' from 2 to 0 over the total number of iterations.

procedureThe study utilizes maximum iteration and saturation for consecutive iterations as the termination criteria for the Whale Optimization Algorithm (WOA), whichever occurs first.

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 5 facts

referenceGuo W, Liu T, Dai F, and Xu P (2020) introduced an improved whale optimization algorithm specifically for feature selection tasks.

referenceLi Y, Han T, Han B, Zhao H, and Wei Z (2019) presented a whale optimization algorithm utilizing a chaos strategy and weight factor at the Journal of Physics: Conference Series.

claimIn the context of energy management optimization, WOA stands for Whale optimization algorithm.

referenceLi Y, Han T, Han B, Zhao H, and Wei Z presented 'Whale optimization algorithm with chaos strategy and weight factor' at the Journal of Physics: Conference Series in 2019.

referenceGuo W, Liu T, Dai F, and Xu P (2020) published 'An improved whale optimization algorithm for feature selection' in Computer Materials and Continua, volume 62, pages 337–354, which presents an improved version of the whale optimization algorithm applied to feature selection.