Ant Colony Search Algorithm Research Articles

A cooperative adaptive genetic algorithm for reentrant hybrid flow shop scheduling with sequence-dependent setup time and limited buffers

This paper deals with a reentrant hybrid flow shop problem with sequence-dependent setup time and limited buffers where there are multiple unrelated parallel machines at each stage. A mathematical model with the minimization of total weighted completion time is constructed for this problem. Considering the complexity of the problem at hand, an effective cooperative adaptive genetic algorithm (CAGA) is proposed. In the algorithm, a dual chain coding scheme and a staged-hierarchical decoding approach are, respectively, designed to encode and decode each solution. Six dispatch heuristics and a dynamic adjustment method are introduced to define initial population. To balance the exploration and exploitation abilities, three effective operations are implemented: (1) two new crossover and mutation operators with collaborative mechanism are imposed on genetic algorithm; (2) an adaptive adjustment strategy is introduced to re-optimize better solutions after mutation operations, where ant colony search algorithm and modified greedy heuristic are intelligently switched; (3) a reset strategy with dynamic variable step strategy is embedded to re-generate some non-improved solutions. A Taguchi method of design of experiment is adopted to calibrate the parameter values in the presented algorithm. Comparison experiments are executed on test instances with different scale problems. Computational results show that the proposed CAGA is more effective and efficient than several well-known algorithms in solving the studied problem.

Real Power Loss Reduction by Ant Colony Search Algorithm

Real Power Loss Reduction by Ant Colony Search Algorithm

Evaluation of Economic Load Dispatch Problem in Power Generating Stations by the Use of Ant Colony Search Algorithms

Evaluation of Economic Load Dispatch Problem in Power Generating Stations by the Use of Ant Colony Search Algorithms

Heuristic algorithms for maximising the total profit of end-of-life computer remanufacturing

Recently the optimisation of end-of-life (EOL) computer remanufacturing has been highlighted since a big amount of used computers have been disposed of every year. Each part inspected after disassembling EOL computers can have various EOL options such as reuse, repair, reconditioning and so on. Depending on EOL options, recovered values and costs of parts will be different. Hence, in order to maximise the profit of remanufactured computers, it is important to develop the method as to how to decide the EOL options of computer parts. To this end, this study deals with a decision-making problem to select the best EOL option policy of the computer parts for maximising the total profit of computer remanufacturing considering its incurred costs and demand of remanufactured computers during multiple production periods. In particular, to maximise the total profit, the conditional repair option is newly proposed. To resolve the problem, a genetic search algorithm and an ant colony search algorithm have been developed. Computational experiments have carried out to evaluate the algorithms and the proposed conditional repair option.

Optimal Unit Commitment with Ant Colony Algorithm

This paper presents an improved ant colony search algorithm that is suitable for solving unit commitment (UC) problems. Ant colony algorithm (ACA) is a meta-heuristic technique for solving hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback, distributed computation as well as constructive greedy heuristic. The ACA was inspired by the behavior of real ants that are capable of finding the shortest path from food sources to the nest without using visual cues. The constraints used in the solution of the UC problem using this approach are: real power balance, real power operating limits of generating units, spinning reserve, startup cost, and minimum up and down time constraints. The approach determines the units schedule followed by the consideration of unit transition related constraints. The proposed approach is expected to yield a better operational cost for the UC problem of production of 50 power plant units. UC problem limits the application of traditional optimization techniques to small size system. Recently, meta-heuristic approaches became popular in the effort to overcome shortcomings of traditional optimization techniques. Techniques such as genetic algorithms, evolutionary programming, simulated annealing, and tabu search have been widely investigated to solve the UC problem. These methods can accommodate more complicated constraints and are claimed to produce solutions of improved quality. The ACA is a meta-heuristic technique proposed to find a near optimal solution to the UC problem. The ACA was inspired by the behavior of real ants which are capable of finding the shortest path from food sources to the nest without using visual cues. The technique has been tested successfully on diverse complex combinatorial optimization problems. Also, it has been shown that the ant colony method performs with little variability over problem diversity or random number seed. In addition, the ACA introduces an entirely new solution at each iteration, while other meta-heuristic optimization techniques are based on improvement of the solution or set of solutions obtained from the previous iteration. During the solution process of the UC problem using the ACA, the obtained solution is always feasible. Moreover, the ACA does not require major assumptions and approximations that limit the solution space.

Optimization Of Combined Economic and Emission Dispatch Problem – A Comparative Study for 30 Bus Systems

This paper presents an evolutionary computation (EC) method called Genetic Algorithm (GA) and a metaheuristic algorithm called Ant Colony Search Algorithm (ACSA) to solve the combined Economic and Emission dispatch (EED) problem with transmission losses. Economic Load Dispatch (ELD) and Economic Emission Dispatch (EED) have been applied to obtain optimal fuel cost and optimal emission of generating units, respectively. Combined Economic Emission Dispatch (CEED) problem is obtained by considering both the economy and emission objectives. A real coded GA has been implemented to minimize both the dispatch cost as well as emission while satisfying all the equality and inequality constraints. ACSA is also developed to provide a means of comparison and it is a new cooperative agents approach, which is inspired by the observation of the behaviors of real ant colonies on the topic of ant trail formation and foraging methods. In the ACSA, a set of cooperating agents called ants cooperates to find a good solution for economic dispatch problem. The merits of ACSA are parallel search and optimization capabilities. The feasibility of the proposed method is tested on a power system network and the experimental results of both GA and ACSA are compared with the solutions of conventional Lamda iteration method.

Ant Colony Search Algorithm for Optimal Generators Startup during Power System Restoration

Generators startup sequence plays a significant role in achieving a suitable and effective restoration strategy. This paper outlines an ant colony search algorithm in order to determine the generator starting times during the bulk power system restoration. The algorithm attempts to maximize the system generation capability over a restoration period, where the dynamic characteristics of different types of units and system constraints are considered. Applying this method for the 39‐bus New England test system, and comparing the results with backtracking‐search and P/t methods, it is found that proposed algorithm improved generation capability.

Application of the ant colony search algorithm to reactive power pricing in an open electricity market

Reactive power management is essential to transfer real energy and support power system security. Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this paper, the production cost of reactive power and investment cost of capacitor banks were included into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. Marginal price theory was used for calculation of the cost of active and reactive power at each bus in competitive electric markets. Application of the proposed method on IEEE 14-bus system confirms its validity and effectiveness. Results from several case studies show clearly the effects of various factors on reactive power price.

Augmented Lagrange—Hopfield Network for Economic Load Dispatch with Combined Heat and Power

This article proposes an augmented Lagrange–Hopfield network for the combined heat and power economic dispatch problem. The augmented Lagrange–Hopfield network method is the continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation. In the proposed augmented Lagrange–Hopfield network, the energy function is augmented by Hopfield terms from the Hopfield neural network and penalty factors from the augmented Lagrangian function to damp out oscillation of the Hopfield network during the convergence process, leading to a fast convergence. The proposed augmented Lagrange–Hopfield network has been tested on various systems and compared to Lagrangian relaxation, the genetic algorithm, the improved ant colony search algorithm, evolutionary programming, the improved genetic algorithm with multiplier updating, and the harmony search algorithm. Test results indicate that the proposed neural network is better than the other methods due to a lower total cost and faster computational time, especially for large-scale combined heat and power economic dispatch problems.

Application of the Ant Colony Search Algorithm to Reactive Power Pricing in an Open Electricity Market

Developing an accurate and feasible method for reactive power pricing is important in the electricity market. In conventional optimal power flow models the production cost of reactive power was ignored. In this study, the production cost of reactive power was comprised into the objective function of the OPF problem. Then, using ant colony search algorithm, the optimal problem was solved. The IEEE 14-bus system has been used for application of the method. The results from several study cases show clearly the effects of various factors on reactive power price.

Application of the Ant Colony Search Algorithm to Reactive Power Pricing in an Open Electricity Market

Application of the Ant Colony Search Algorithm to Reactive Power Pricing in an Open Electricity Market

Reconfiguration and Capacitor Placement for Loss Reduction of Distribution Systems by Ant Colony Search Algorithm

This paper aims to study distribution system operations by the ant colony search algorithm (ACSA). The objective of this study is to present new algorithms for solving the optimal feeder reconfiguration problem, the optimal capacitor placement problem, and the problem of a combination of the two. The ACSA is a relatively new and powerful swarm intelligence method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as ldquogreedyrdquo search. The ACSA was inspired from the natural behavior of ants in locating food sources and bring them back to their colony by the formation of unique trails. Therefore, through a collection of cooperative agents called ldquoants,rdquo the near-optimal solution to the feeder reconfiguration and capacitor placement problems can be effectively achieved. In addition, the ACSA applies the state transition, local pheromone-updating, and global pheromone-updating rules to facilitate the computation. Through simultaneous operation of population agents, process stagnation can be effectively prevented. Optimization capability can be significantly enhanced. The proposed approach is demonstrated using two example systems from the literature. Computational results show that simultaneously taking into account both feeder reconfiguration and capacitor placement is more effective than considering them separately.

Optimal Electrical Distribution Systems Reinforcement Planning Using Gas Micro Turbines by Dynamic Ant Colony Search Algorithm

Distribution systems management is becoming an increasingly complicated issue due to the introduction of new energy trading strategies and new technologies. In this paper, an optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. In the new deregulated energy market and considering the incentives coming from the political and economical fields, it is reasonable to consider distributed generation (DG) as a viable option for systems reinforcement. In the paper, the DG technology is considered as a possible solution for distribution systems capacity problems, along several years. Therefore, compound solutions comprising the installation of both feeders and substations reinforcement and DG integration at different times are considered in the formulation of a minimum cost distribution systems reinforcement strategy problem. An application on a medium size network, hypothesizing a scenario of reinforcement also using as DG gas micro-turbines, is carried out using a novel optimization technique allowing the identification of optimal paths in trees or graphs. The proposed technique is the Dynamic Ant Colony Search algorithm

06/00915 Distribution network reconfiguration for loss reduction by ant colony search algorithm: Su, C.-T. et al. Electric Power Systems Research, 2005, 75, (2–3), 190–199.

06/00915 Distribution network reconfiguration for loss reduction by ant colony search algorithm: Su, C.-T. et al. Electric Power Systems Research, 2005, 75, (2–3), 190–199.

Adaptive and Dynamic Ant Colony Search Algorithm for Optimal Distribution Systems Reinforcement Strategy

The metaheuristic technique of Ant Colony Search has been revised here in order to deal with dynamic search optimization problems having a large search space and mixed integer variables. The problem to which it has been applied is an electrical distribution systems management problem. This kind of issues is indeed getting increasingly complicated due to the introduction of new energy trading strategies, new environmental constraints and new technologies. In particular, in this paper, the problem of finding the optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. Utilities indeed need efficient software tools to take decisions in this new complex scenario. In past times, utilities project the load growth for several years and then estimate when the capacity limit will be exceeded. Designers then consider some feasible alternatives and select the optimal one in terms of performance and costs. In this paper, the Distributed Generation, DG, technology considered in compound solutions with the installation of feeder and substations is viewed as a new option for solving distribution systems capacity problems, along several years. The objective to be minimized is therefore the overall cost of distribution systems reinforcement strategy in a given timeframe. An application on a medium size network is carried out using the proposed technique that allows the identification of optimal paths in extremely large or non-finite spaces. The proposed algorithm uses an adaptive parameter in order to push exploration or exploitation as the search procedure stops in a local minimum. The algorithm allows the easy investigation of these kinds of complex problems, and allows to make useful comparisons as the intervention strategy and type of DG sources vary.

Ant colony search algorithm for optimal reactive power optimization

The paper presents an (ACSA) Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents? approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical) test bus system. The proposed approach is tested and compared to genetic algorithm (GA), Adaptive Genetic Algorithm (AGA).

Ant Colony Search Algorithm for Optimal Strategical Planning of Electrical Distribution Systems Expansion

Strategical planning is one of many research fields in the design of electrical distribution systems. The problem of strategical planning is a multiobjective combinatorial problem and the search space may often be quite large concerning to the options. The aim is to identify a strategy of expansion of a given distribution system in a given timeframe. For this problem, the search space is created beforehand by running a multiobjective optimisation algorithm for the optimal design of distribution networks for different load levels related to different years. The sets of Pareto-optimal solutions obtained for each load level at each year are equivalent in terms of the considered objectives, these being minimum losses, installation costs, and minimum unavailability. The problem of the identification of the optimal expansion strategy through these chronologically intermediate solutions leading to the final target configuration at the last year has been solved herein using an ACS (Ant Colony Search) algorithm. In order to verify the efficiency of the ACS algorithm, a small size application has been carried out and results have been compared to those obtained with enumeration. Then, a Simulated Annealing (SA) approach was used for a larger size test problem and results were compared to those obtained using the ACS. For this problem, the ACS demonstrated to be more robust than SA with higher quality results.

Optimal Capacitor Placement in Distribution Systems Employing Ant Colony Search Algorithm

This article introduces an ant colony search algorithm (ACSA) to solve the optimal capacitor placement problem. This ACSA is a relatively new meta-heuristic for solving hard combinational optimization problems. It is a population-based approach that uses exploration of positive feedback as well as greedy search. The ACSA was inspired from the natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. Therefore, through a collection of cooperative agents called ants, the near-optimal solution to the capacitor placement problem can be effectively achieved. In addition, in the algorithm, the state transition rule, local pheromone-updating rule, and global pheromone-updating rule are all added to facilitate the computation. Through operating the population of agents simultaneously, the process stagnation can be effectively prevented. Namely the optimization capability can thus be significantly enhanced. Moreover, the capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints, which is commonly solved by employing mathematical programming methods, and will be solved using ACSA in this article. The proposed approach is demonstrated employing two application examples. Numerical results of a small-size example system show that the proposed method can achieve an optimal solution like the exhaustive search, but with much less computational burden. Also, this proposed method is superior to some other methods adopted herein in terms of power loss and costs.

Edge detection using ant algorithms

In this paper a new algorithm for edge detection using ant colony search is proposed. The problem is represented by a directed graph in which nodes are the pixels of an image. To adapt the problem, some modifications on original ant colony search algorithm (ACSA) are applied. A large number of experiments are employed to determine suitable algorithm parameters. We drive an experimental relationship between the size of the image to be analyzed and algorithm parameters. Several experiments are made and the results suggest the effectiveness of the proposed algorithm.

Distribution network reconfiguration for loss reduction by ant colony search algorithm

This paper introduces an ant colony search algorithm (ACSA) to solve the optimal network reconfiguration problem for power loss reduction. The ACSA is a relatively new and powerful intelligence evolution method for solving optimization problems. It is a population-based approach that uses exploration of positive feedback as well as greedy search. The ACSA was inspired from natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trail formation. By applying the ACSA, the near-optimal solution to the network reconfiguration problem can be effectively achieved. The ACSA applies the state transition rule, local pheromone-updating rule, and global pheromone-updating rule to facilitate the computation. The network reconfiguration problem of one three-feeder distribution system from the literature and one practical distribution network of Taiwan Power Company (TPC) are, respectively, solved using the proposed ACSA method, the genetic algorithm (GA), and the simulated annealing (SA). Numerical results show that the proposed method is better than the other two methods.