Sharif Digital Repository

The major application of stochastic intelligent methods in optimisation, control and management of complex systems is transparent. Many researchers try to develop collective intelligent techniques and hybrid meta-heuristic models for improving the reliability of such optimisation algorithms. In this paper, a new optimisation method that is the simulation of 'the great salmon run' (TGSR) is developed. This simulation provides a powerful tool for optimising complex multi-dimensional and multi-modal problems. For demonstrating the high robustness and acceptable quality of TGSR, it is compared with most of the well-known proposed optimisation techniques such as parallel migrating genetic... 

Transportation network design problem (TNDP) aims to choose from among a set of alternatives (e.g., set of new arcs) which minimizes an objective (e.g., total travel time), while keeping consumption of resources (e.g., budget) within their limits. TNDP is formulated as a bilevel programming problem, which is difficult to solve on account of its combinatorial nature. Following a recent, heuristic by ant colony optimization (ACO), a hybridized ACO (HACO) has been devised and tested on the network of Sioux Falls, showing that the hybrid is more effective to solve the problem. In this paper, employing the heuristic of particle swarm optimization (PSO), an algorithm is designed to solve the TNDP.... 

Time, cost and quality are among the major objectives of any project. In recent years, the demands of project stakeholders regarding reductions in the total cost and time of a project along with achieving the desirable quality of the project have risen significantly. This leads researchers to developing models that incorporate the quality factor to previously existing time-cost trade-off models. This study presents a model for the discrete time-cost-quality trade-off problem. In this model there are a number of execution modes to select for each activity, and the best execution mode (t, c, q) of the activities should be determined to optimize total cost and time objectives, subjected to... 

Graph sum colouring problem is a special class of graph vertex colouring problem. Because of its various applications in practical areas, especially in scheduling, many researchers have been focused on it during the past decade. In recent years, several heuristic and meta-heuristic algorithms have been developed to solve sum colouring problem. In this research, a hybrid algorithm based on mini-max ant system and simulated annealing is applied for the problem. This algorithm is tested on benchmark random graphs and compared to the previous algorithms. Results show that in many cases, the best known results can be obtained or improved by the proposed algorithm. Copyright © 2019 Inderscience... 

Graph sum colouring problem is a special class of graph vertex colouring problem. Because of its various applications in practical areas, especially in scheduling, many researchers have been focused on it during the past decade. In recent years, several heuristic and meta-heuristic algorithms have been developed to solve sum colouring problem. In this research, a hybrid algorithm based on mini-max ant system and simulated annealing is applied for the problem. This algorithm is tested on benchmark random graphs and compared to the previous algorithms. Results show that in many cases, the best known results can be obtained or improved by the proposed algorithm. Copyright © 2019 Inderscience... 

In this paper, a new algorithm for solving the shortest loop design problem is presented. The shortest loop design problem is to find the shortest loop for an automated guided vehicle covering at least one edge of each department of a block layout. In this paper, first it is shown that this problem can be represented as a graph model. The properties of the presented model enable us to design a meta-heuristic based on ant colony system algorithm for solving the shortest loop design problem. Computational results show the efficiency of our algorithm in compare to the other techniques  

The graph colouring problem, as an important NP-complete problem, is considered in this paper and a hybrid meta-heuristic approach is developed to solve it. The initial solution of the algorithm, generated by a heuristic method, is used by a simulated annealing (SA) approach to generate new solutions until no progress in a number of solutions reported. At this stage, the algorithm will use a tabu search routine and this local search operator will be followed for some iterations. After finding a better solution, the algorithm is again followed through SA. Efficiency of the algorithm is showed through various experiments on well-known benchmark problems of DIMACS. Comparison with the available... 

Non-Convex Economic Dispatch (NED) has been addressed as an open and demanding optimization problem in power systems. Due to the fact that realistic ED problems have non-convex cost functions with equality and inequality constraints, conventional search methods are unable to effectively find the global solution. In recent years, because of their great potential to achieve optimal or close-to-optimal solution, meta-heuristic optimization techniques have attracted significant attention to tackle the complexity of NED problems. In this paper, an efficient approach is proposed based on Imperialist Competitive Algorithm (ICA). The proposed algorithm named multi-operator ICA (MuICA) merges the... 

This paper discusses a bi-objective reliability optimization of a switch-mode active and cold standby k-out-of-n series–parallel system in which a switch is installed to add a redundant component when one of the components fails. The system failure rate is not only dependent on the number of its working components, but reduces when more monitory resources are allocated. As the bi-objective optimization problem is shown to belong to the class of NP-hard problems, a multi-objective meta-heuristic algorithm, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) with a novel solution structure is developed to solve large-scale problems. Since there is no benchmark... 

Node cooperation can protect wireless networks from eavesdropping by using the physical characteristics of wireless channels rather than cryptographic methods. Allocating the proper amount of power to cooperative nodes is a challenging task. In this paper, we use three cooperative nodes, one as relay to increase throughput at the destination and two friendly jammers to degrade eavesdropper’s link. For this scenario, the secrecy rate function is a non-linear non-convex problem. So, in this case, exact optimization methods can only achieve suboptimal solution. In this paper, we applied different meta-heuristic optimization techniques, like Genetic Algorithm (GA), Partial Swarm Optimization... 

Many chemical production systems encounter with a functionally flexible workplace which need multi-skilled workers. This research integrates the multi-skilled project scheduling problem with the resource investment problem which aims to obtain concurrent optimal policy of project scheduling and skills’ recruitment. The objective of the proposed model is minimization of total recruitment cost for different levels of the skills. An integer programming formulation is developed for the problem, and it is validated through solving several small scale instances using the GAMS software. To tackle real-life problem scales, a genetic-based and a particle-swarm-based algorithm with calibrated... 

The focus of this research is on the flexible flow shop sequence dependent group scheduling problem (FFSDGS) with minimization of makespan as the criterion (FFm|fmls, Splk|Cmax). Since the research problem is shown to be NP-hard, a meta-heuristic algorithms based on memetic algorithm is developed to efficiently solve the problem. The performance of the proposed algorithm is compared with the only available meta-heuristic algorithm in the literature, i.e., tabu search (TS). The comparison is performed based on paired t-test and the result shows that the memetic algorithm has a superior performance compared to the TS  

In this research, a new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems. The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs. The NNA benefits from complicated structure of the ANNs and its operators in order to generate new candidate solutions. In terms of convergence proof, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of NNA is proved theoretically. In this paper, the NNA with its interconnected computing unit is examined... 

Short term hydrothermal scheduling (STHTS) is a non-linear, multi-modal and very complex constrained optimization problem which has been solved using several conventional and modern metaheuristic optimization algorithms. A number of research articles have been published addressing STHTS using different techniques. This article presents a comprehensive review of research published for solving the STHTS problem in the last four decades. © 2013 IEEE