General Ant Colony Optimization Research Articles

Convergence Speed of Multi-Objective Generalized Ant Colony Optimization Algorithm

Multi-objective optimization problems are very important, but it is hard to optimized or solved. Generalized ant colony optimization (GACO) algorithm is a new kind of ant colony optimization (ACO) algorithm developed in recent years. In this paper, we try to combine Multi-objective optimization problems with GACO algorithm, established a model for multi-objective GACO algorithm by absorbing state Markov chain, and present a method for estimating the convergence speed of multi-objective GACO algorithm. Simulation results show that the convergence speed of multi-objective GACO algorithm is faster than traditional multi-objective ACO algorithm.

Analysis of Convergence Rate for Improved Generalized Ant Colony Optimization Algorithm

Although theoretical result of convergence for improved generalized ant colony optimization (IGACO) algorithm has been proved in recent years, the convergence speed is also an open and difficult problem. This article, based on the Markov model, tries to explore the analysis of convergence speed for IGACO algorithm. Some experiments have been studied to compare the convergence speed between ant colony optimization (ACO) algorithm and IGACO algorithm.

Adaptive Clustering based Dynamic Routing of Wireless Sensor Networks via Generalized Ant Colony Optimization

Wireless sensor networks (WSNs) use battery-powered sensor nodes for sensing, thus the energy efficiency is critical to extend the lifespan. The performance depends on the trade-off among energy consumption, latency and reliability. Data aggregation is a fundamental approach to eliminate redundancy and minimize transmission cost so as to save energy. Dynamic clustering based routing is proposed to achieve good performance via adaptive algorithms. The generalized Ant Colony Optimization (ACO) is applied to increase the reliable lifespan of sensor nodes with energy constraints. Each sensor node is modeled as an artificial ant and dynamic routing is modeled as ant foraging. The ant pheromone is released when an energy efficient channel from the source to sink is secured. Route discovery, data aggregation and information loss are modeled as the processes of pheromone diffusion, accumulation and evaporation. Each sensor node estimates the residual energy and dynamically calculates probabilities to select an optimal channel to extend the lifespan of WSNs.

A fuzzy based secure QoS routing protocol using ant colony optimization for mobile Ad hoc network

Ensuring secure communication in mobile ad hoc network (MANET) is the main challenging task due to the network characteristics. In this paper, we apply fuzzy logic method and method of ant colony optimization (ACO) to establish a secure routing path with minimum delay for mobile ad hoc network. The ACO algorithm is used to establish a virtual backbone connecting cluster heads (CHs). The general ACO algorithm is modified to allow selection of routing path, based on trust value of the nodes lying in the routing path and delaying experienced in the path. The procedure uses a fuzzy logic controller to evaluate the trust value of the nodes based on node's QoS parameters: energy drain rate, lifetime of the node, packet drop rate and packet forwarding status of the node. The behavior of the protocol under various attacks is analyzed. Experimentation shows that the algorithm results in secure shortest routing path for data communication. Additionally, the packet drop rate and routing delay of the proposed routing protocol in presence of malicious nodes is fewer compared to secure cluster based routing protocol (SCBRP) and Fuzzy Logic based Security Level routing protocol (FLSL).

Recurrent fuzzy system design using elite-guided continuous ant colony optimization

This paper proposes recurrent fuzzy system design using elite-guided continuous ant colony optimization (ECACO). The designed recurrent fuzzy system is the Takagi–Sugeno–Kang (TSK)-type recurrent fuzzy network (TRFN), in which each fuzzy rule contains feedback loops to handle dynamic system processing problems. The ECACO optimizes all of the free parameters in each recurrent fuzzy rule in a TRFN. Unlike the general ant colony optimization that finds solutions in discrete space, the ECACO finds solutions in a continuous space. The ECACO is a population-based optimization algorithm. New solutions are generated by selection, Gaussian random sampling, and elite-guided movement. To verify the performance of ECACO, three examples of dynamic plant control are simulated using ECACO-optimized TRFNs. The ECACO performance is also compared with other continuous ant colony optimization, particle swarm optimization, and genetic algorithms in these simulations.

Two Hybrid Metaheuristic Algorithms for Hot Rolling Scheduling

This paper presents a model for the hot rolling scheduling problem (HRSP), which is derived from the actual steel production. The model is characterized by some new features, such as the rolling length of the consecutive slabs with the same width, temperature jump between adjacent slabs. The warm up part is a minor part of a turn and is usually ignored, but it affects directly the product quality. Therefore, besides the slab sequence in the staple material section, we also consider the slab sequence in the warm up material section. These features make the solution methodology more difficult. Therefore, two hybrid strategies are proposed to determine good approximate solutions for this complicated problem. The first one (CT_ACO) is a hybrid strategy based on the solution construction mechanism of ant colony optimization (ACO) with cyclic transfers (CT). The second one (CT_SS) is to hybridize scatter search (SS) and CT neighborhood search. Moreover, we design a decision support system in which two algorithms have been embedded for the HRSP. The most popular feature of the system is the architecture of component management, which allows us to modify easily some components according to the practice situation. The computational experiments show that CT_ACO is superior to general ACO, and CT_SS is also better than SS in terms of solution quality. The CT_ACO method and CT_SS have more potential for improvement to solve the HRSP compared with the current scheduling method, and the CT_ACO generates slightly better quality solutions than CT_SS algorithm.

Optimal groundwater monitoring design using an ant colony optimization paradigm

Groundwater long-term monitoring (LTM) is required to assess the performance of groundwater remediation and human being health risk at post-closure sites where groundwater contaminants are still present. The large number of sampling locations can make the LTM costly, especially since LTM may be required over several decades. An optimization algorithm based on the ant colony optimization (ACO) paradigm is developed to minimize the overall data loss due to fewer sampling locations for a given number of monitoring wells. The ACO method is inspired by the ability of an ant colony to identify the shortest route between their nest and a food source. The developed ACO-LTM algorithm is applied to a field site with an existing 30-well LTM network. When compared to the results identified through complete enumeration, the ACO-LTM solutions are globally optimal for the cases with 21 to 27 remaining wells. Results from the developed ACO-LTM algorithm provide a proof-of-concept for the application of the general ACO analogy to the groundwater LTM sampling location optimization problem. A major contribution of this work is the successful development of an efficient and effective stochastic search algorithm for solving the LTM optimization problem based on the ACO paradigm.

Ant colony optimization algorithm with random perturbation behavior to the problem of optimal unit commitment with probabilistic spinning reserve determination

In this paper, a novel ant colony optimization algorithm with random perturbation behavior (RPACO) based on combination of general ant colony optimization and stochastic mechanism is developed for the solution of optimal unit commitment (UC) with probabilistic spinning reserve determination. In general, the purpose of UC is to enhance the economical efficiency as could as possible while simultaneously satisfying physical and operation constraints of individual unit. Consider the possibility of generating unit failure, the requirement, the sufficient spinning reserve capacity to ensure adequate reliability levels, must be satisfied by the commitment schedule. The security function approach is applied to evaluate the desired level of system security, and the proposed method in this paper, RPACO, is adopted to solve the UC problems. The effectiveness of the proposed method has been demonstrated on the corresponding numerical results. Further, the sensitivity of the desired security level to the optima during optimization is investigated in this paper.