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Features | PSO | GA | ACO | AIS | ABC | BFA |
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Representation | Dimensional vector for position speed, the best state | Binary, real list of rules, permutation of elements | Undirected graph | Attribute str. (a real-valued vector), integer string, binary string symbolic string | D-dimensional vector (xi = 1, 2, …, D) | Represents i-th bacterium at jth chemotactic, k-th reproductive, and l-th dispersal step |
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Operators | initializer, update, and evaluator | Crossover, mutation, selection, inversion | Pheromone update and measure, trail evaporation | Immune operators cloning, hypermutation and selection based on elitism | Reproduction, replacement of bee, selection | Reproduction, chemotaxis, dispersion, elimination |
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Datasets for attack detection | KDDCUP99, DARPA98, NSL-KDD | KDDCUP99, DARPA98 | KDDCUP99, DARPA98 | KDDCUP99, DARPA98, LLDOS | KDDCUP99, DARPA98 | KDDCUP99, DARPA98 |
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Permittivity against DoS attack | High | Low | High | Low | Low | Low |
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Structure and dynamics | Discrete and network components and evolution or learning based | Discrete and network components and evolution based | Discrete components and evolution and learning based | Discrete and network components and evolution or learning based | Discrete and components and evolution based | Discrete and network components and evolution or learning based |
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Reliability | High | Low | High | High | Low | Low |
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Time-consuming | Time-consuming because of its complexity but the no. of repetitions is less | Low time-consuming because of its simplicity but the no. of repetitions is high | Time-consuming because of its complexity | Low time-consuming because of its simplicity | Low time-consuming because of its simplicity | Low time-consuming because of its simplicity |
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Robustness | High | Low as compared to PSO, but more than others | Lower than PSO and GA, more than others | Lower than ACO better | Lowest | Higher than ABS |
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Parameters | Number of particles, dimension of particles, range of particles, maximum number of iterations, inertia weight | Population size, max generation number, cross-over probability | Number of ants, iterations, pheromone evaporation rate, amount of reinforcement | Population size, no. of antibodies to be selected for hypermutation, number of antibodies to be replaced | No. of food sources which is equal to the no. of employed onlooker bees | The dimension of the search space, number of bacteria, number of steps chemotactic, no. of elimination and dispersal events, no. of reproduction steps, probability |
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Applications | Power system optimization problems, multimodel problems, multiobjective, dynamic, constrained, and combinatorial optimization problems, anomaly detection, sequential ordering problem, etc. | Pattern recognition, reactive power dispatch, sensor-based robot path planning, multiobjective vehicle routing problem, molecular modeling, web service selection, etc. | Continuous optimization and parallel processing implementations. Vehicle routing problem, graph coloring and set covering, agent-based dynamic scheduling, etc. | Computer security, anomaly detection, clustering/classification, numeric function optimization, virus detection, pattern recognition, etc. | Solving reliability redundancy allocation problem, training neural networks, XOR, decoder-encoder, and 3-bit parity benchmark problems, pattern classification, etc. | Application for harmonic estimation problem in power systems, the parameters of membership functions, and the weights of rules of a fuzzy rule set are estimated, etc. |
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