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Algorithms

NiaARM.jl implements several population-based metaheuristics. All optimizers share a common signature (feval, problem, stoppingcriterion; kwargs...) where feval is narm.

Differential Evolution (DE)

  • Entry point: de
  • Strategy: DE/rand/1/bin with greedy selection.
  • Parameters: popsize, cr (crossover rate), f (differential weight).
  • Reference: Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.

Particle Swarm Optimization (PSO)

  • Entry point: pso
  • Inertia-weight PSO with personal and global best attraction.
  • Parameters: popsize, omega, c1, c2.
  • Reference: Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, 4, 1942-1948.

Genetic Algorithm (GA)

  • Entry point: ga
  • Tournament selection, uniform crossover, and per-gene mutation.
  • Parameters: popsize, tournament_size, crossover_rate, mutation_rate.
  • Reference: Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press.

Simulated Annealing (SA)

  • Entry point: sa
  • Gaussian perturbations with multiplicative cooling.
  • Parameters: initial_temp, cooling_rate, step_size.
  • Reference: Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.

Bat Algorithm (BA)

  • Entry point: ba
  • Frequency-tuned velocity updates with adaptive loudness and pulse rate.
  • Parameters: popsize, fmin, fmax, loudness0, pulse_rate0, alpha, gamma.
  • Reference: Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (pp. 65-74). Springer.

Artificial Bee Colony (ABC)

  • Entry point: abc
  • Worker/onlooker/scout phases over food sources.
  • Parameters: popsize, limit.
  • Reference: Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department.

Cuckoo Search (CS)

  • Entry point: cs
  • Lévy flights with abandonment probability pa.
  • Parameters: popsize, pa.
  • Reference: Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), 210-214.

Firefly Algorithm (FA)

  • Entry point: fa
  • Attraction decays exponentially with distance; random walks shrink via theta.
  • Parameters: popsize, alpha, beta0, gamma, theta.
  • Reference: Yang, X. S. (2008). Firefly algorithm. In Nature-Inspired Metaheuristic Algorithms (pp. 79-90). Luniver Press.

Flower Pollination Algorithm (FPA)

  • Entry point: fpa
  • Switches between global Lévy flights and local pollination using probability p.
  • Parameters: popsize, p.
  • Reference: Yang, X. S. (2012). Flower pollination algorithm for global optimization. In Unconventional Computation and Natural Computation (pp. 240-249). Springer.

L-SHADE

  • Entry point: lshade
  • Current-to-pbest/1/bin DE with success-history adaptation and population reduction.
  • Parameters: popsize, memorysize, pbestrate, archiverate (requires StoppingCriterion.maxevals to be set).
  • Reference: Tanabe, R., & Fukunaga, A. (2014). Improving the search performance of SHADE using linear population size reduction. 2014 IEEE Congress on Evolutionary Computation (CEC), 1658-1665.

Evolution Strategy (ES)

  • Entry point: es
  • Self-adaptive step sizes with log-normal mutations in a (μ, λ) setting.
  • Parameters: mu, lambda, sigmainit, tau, tauprime.
  • Reference: Schwefel, H. P. (1977). Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Birkhäuser.
  • Entry point: randomsearch
  • Generic stochastic search used as a simple baseline optimizer.