MetaheuristicA high-level problem-solving strategy that guides the search process for near-optimal solutions, applicable to a wide range of optimization problems without requiring problem-specific knowledge.
PheromoneIn ACO, a virtual chemical trail deposited by artificial ants on edges of a solution graph, used to communicate information about solution quality to other ants through indirect (stigmergic) communication.
StigmergyIndirect communication between agents through modifications to the shared environment, the mechanism used by real ants (pheromone trails) and the foundation of Ant Colony Optimization.
Fitness FunctionA function that evaluates the quality of a candidate solution, analogous to biological fitness. Swarm algorithms seek to maximize (or minimize) this function through collective search.
Exploration vs ExploitationThe fundamental trade-off in optimization between exploring new regions of the search space (exploration) and refining known good solutions (exploitation). Effective swarm algorithms balance both.
ConvergenceThe process by which the swarm's candidate solutions progressively concentrate around optimal or near-optimal regions of the search space, ideally approaching the global optimum.
PopulationThe collection of all candidate solutions (particles, ants, bees, wolves) that simultaneously search the solution space, sharing information to guide the collective search.
Velocity UpdateIn PSO, the rule that adjusts each particle's movement direction and speed based on its personal best position, the global best position, and random factors.
Evaporation RateIn ACO, the rate at which pheromone trails decay over time, preventing the algorithm from converging too quickly on suboptimal solutions and maintaining exploration.
Waggle DanceIn the ABC algorithm and real bee colonies, a communication mechanism where bees share information about the location and quality of food sources with other colony members.
Traveling Salesman Problem (TSP)A classic NP-hard combinatorial optimization problem asking for the shortest route visiting each city exactly once, a benchmark problem for ACO and other swarm algorithms.
Inertia WeightA parameter in PSO that controls the influence of a particle's previous velocity on its current movement, balancing momentum (exploration) with responsiveness (exploitation).
Grey Wolf HierarchyIn GWO, the social hierarchy of alpha (best solution), beta (second best), delta (third best), and omega (remaining) wolves that guides the search process.
NP-HardA class of computational problems for which no efficient (polynomial-time) algorithm is known, and for which metaheuristics like swarm algorithms provide practical approximate solutions.
Swarm RoboticsThe application of swarm intelligence principles to physical multi-robot systems, where teams of simple robots coordinate through local interactions to accomplish collective tasks.
Local OptimaA solution that is better than all neighboring solutions but not necessarily the best overall (global optimum). Avoiding premature convergence to local optima is a key challenge for all swarm algorithms.
Global OptimumThe absolute best solution in the entire search space. Finding the global optimum is the ultimate goal of optimization, but proving that a found solution is truly global is often intractable for NP-hard problems.
Levy FlightA random walk pattern with step lengths drawn from a heavy-tailed distribution, used in some swarm algorithms (e.g., cuckoo search) to enable long-range exploration and avoid local optima.
Multi-Objective OptimizationOptimization problems with multiple conflicting objectives where no single solution is best for all objectives. Swarm algorithms are extended to find Pareto-optimal fronts of non-dominated solutions.
Pareto FrontThe set of solutions where improving one objective necessarily worsens another, representing the trade-off boundary in multi-objective optimization. Swarm algorithms are well-suited for discovering Pareto fronts.
Benchmark FunctionA standardized mathematical function (e.g., Rastrigin, Ackley, Rosenbrock) used to test and compare the performance of optimization algorithms across different landscape characteristics.
Adaptive Parameter ControlTechniques that automatically adjust algorithm parameters (population size, inertia weight, pheromone rate) during the search based on convergence progress, reducing the need for manual tuning.
Cuckoo SearchA swarm algorithm inspired by the brood parasitism of cuckoo birds, using Levy flights for exploration and a probability of nest discovery for exploitation, developed by Xin-She Yang and Suash Deb (2009).
Whale Optimization AlgorithmA metaheuristic inspired by the bubble-net hunting strategy of humpback whales, using spiral and shrinking encircling mechanisms, developed by Seyedali Mirjalili (2016).
Bat AlgorithmA swarm optimization method inspired by the echolocation behavior of microbats, where frequency tuning and pulse emission rate control exploration and exploitation, developed by Xin-She Yang (2010).
Self-OrganizationThe spontaneous emergence of ordered patterns and structures from initially random interactions among simple agents, without external direction or central control, a hallmark of swarm intelligence.