Whale Optimization Algorithm (WOA)
Also known as: WOA, Balina Optimizasyon Algoritması (WOA), bubble-net attacking method
The Whale Optimization Algorithm (WOA) is a swarm-based metaheuristic introduced by Mirjalili and Lewis in 2016. It models the bubble-net hunting strategy of humpback whales, in which a group of whales spirals around prey while gradually tightening the encirclement. The algorithm balances global exploration and local exploitation through a small set of parameters and has become widely used in continuous engineering optimisation problems.
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When to use it
WOA is appropriate for continuous optimisation problems where the objective function is costly or analytically intractable — engineering design, structural optimisation, feature selection, and hyperparameter tuning. It requires no gradient information and no assumption of normality. The algorithm works on all data structures and is especially well suited to problems where exploration of a large search space must precede fine local refinement. If the decision variables are discrete or combinatorial, WOA may need adaptation; for such problems Tabu Search or Genetic Algorithms may be more directly applicable.
Strengths & limitations
- Very small parameter set — population size and iteration count are the main controls — making configuration straightforward.
- Balances exploration and exploitation through the encirclement shrinking and spiral mechanisms without requiring manual tuning of that balance.
- Requires no normality assumption and no gradient; applies directly to black-box objective functions.
- Competitive performance on unimodal and multimodal benchmark functions, confirmed by Mirjalili and Lewis (2016) against PSO, GSA, and DE.
- Like all population-based metaheuristics, WOA offers no guarantee of finding the global optimum; it is a heuristic.
- Premature convergence can occur on highly multimodal landscapes if the population is small or the iteration budget is tight.
- Performance on discrete and combinatorial problems requires problem-specific encoding and neighbourhood operators.
- The logarithmic spiral parameter b is fixed during a run; its value affects convergence speed but receives limited theoretical guidance.
Frequently asked
How does WOA differ from Particle Swarm Optimisation (PSO)?
Both are swarm-based and update each agent's position relative to the best-known solution, but WOA adds a spiral trajectory and an encirclement shrinking mechanism inspired by whale hunting, which together give a structured transition from global search to local refinement. PSO uses velocity memory; WOA does not maintain velocities. Mirjalili and Lewis (2016) showed WOA outperforming PSO on several benchmark functions, though results are problem-dependent.
What are the key parameters and how should they be set?
The two primary parameters are population size (commonly 20–50 agents) and maximum iterations (commonly 100–1000 depending on evaluation cost). The logarithmic spiral constant b is typically set to 1 following the original paper. The encirclement coefficient a decreases linearly from 2 to 0 over the run and is not manually tuned. Start with default values and increase population size if premature convergence is suspected.
Can WOA handle multi-objective problems?
The standard WOA as published in 2016 is a single-objective algorithm. Multi-objective variants (MOWOA) have been proposed in subsequent literature that incorporate Pareto archiving and crowding-distance selection, but these are extensions beyond the original algorithm.
How many runs are needed for a reliable result?
Because WOA is stochastic, a single run may not represent typical behaviour. Running 20–30 independent trials with different random seeds and reporting the mean, standard deviation, best, and worst objective values is standard practice in metaheuristic benchmarking, consistent with the comparison methodology used in Mirjalili and Lewis (2016).
Sources
- Mirjalili, S. & Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. DOI: 10.1016/j.advengsoft.2016.01.008 ↗
- Chakraborty, S. et al. (2023). A Comprehensive Review of the Whale Optimization Algorithm: Modifications, Variants, and Applications. Artificial Intelligence Review. link ↗
How to cite this page
ScholarGate. (2026, June 1). Whale Optimization Algorithm (WOA). ScholarGate. https://scholargate.app/en/optimization/whale-optimization
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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