Sharif Digital Repository

This study presents a hybrid harmony search algorithm (HHSA) to solve engineering optimization problems with continuous design variables. Although the harmony search algorithm (HSA) has proven its ability of finding near global regions within a reasonable time, it is comparatively inefficient in performing local search. In this study sequential quadratic programming (SQP) is employed to speed up local search and improve precision of the HSA solutions. Moreover, an empirical study is performed in order to determine the impact of various parameters of the HSA on convergence behavior. Various benchmark engineering optimization problems are used to illustrate the effectiveness and robustness of... 

In deregulated power systems, distribution network operators (DNO) are responsible for maintaining the proper operation and efficiency of distribution networks. This is achieved traditionally through specific investments in network components. The event of distributed generation (DG) has introduced new challenges to these distribution networks. The role of DG units must be correctly assessed to optimise the overall operating and investment cost for the whole system. However, the distributed generation owners (DGOs) have different objective functions which might be contrary to the objectives of DNO. This study presents a long-term dynamic multi-objective model for planning of distribution... 

Enforced hill climbing (EHC), a heuristicaa search method, has been frequently used in a number of AI planning systems. This paper presents a new form of EHC, guided enforced hill climbing (GEHC), to enhance EHC efficiency. Main feature in GEHC is an adaptive ordering function. GEHC has shown a significant improvement in EHC efficiency, especially when applied to larger problems  

Guess-and-determine (GD) attacks are general attacks on stream ciphers, which have often been implemented in an ad hoc manner. The authors introduce a heuristic approach to the design of GD attacks, that is a dynamic programming method using a Viterbi-like algorithm which is a well-known decoding algorithm for convolutional codes. The authors also show that with this method, the resulting GD attacks, named heuristic GD (HGD) attacks, on TIPSY, SNOW1 and SNOW2 lead to less computational complexity than the previously known GD attacks. The main advantage of HGD attacks, over ad hoc GD attacks, is that while being powerful, they can be designed algorithmically for classes of stream ciphers,... 

This paper presents a mathematical programming to model a bi-criteria traveling salesman problem (TSP) with fuzzy time windows. The objective is to minimize the travel time and minimize the sum of salesman's waiting time during the service. A triangle fuzzy number is defined for the service time of each customer. The objective function is normalized in the interval between 0 and 1. To solve such a model, two heuristic methods based on genetic algorithms (GAs) and ant colony optimization (ACO) are developed. To validate and show the efficiency these methods, a number of test problems are generated at random. Finally, a comparison of the obtained results is presented  

Delay in communication wires causes design iterations in system-on-chip. Latency-insensitive design copes with this issue by encapsulating each core in a shell wrapper and inserting buffers in the wires to separate the design of core from that of communication wires. Scheduling-based latency-insensitive protocol is a methodology which employs shift registers for periodic clock gating of blocks instead of the shell wrappers. In many cases, the bit sequences inside the shift registers are too long and therefore consume a large area. This study presents a heuristic algorithm that optimises the bit sequences and produces them with shorter lengths compared with the existing method. The algorithm... 

Fast and high quality document clustering is a crucial task in organizing information, search engine results, enhancing web crawling, and information retrieval or filtering. Recent studies have shown that the most commonly used partition-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we propose a novel Harmony K-means Algorithm (HKA) that deals with document clustering based on Harmony Search (HS) optimization method. It is proved by means of finite Markov chain theory that the HKA converges to the global optimum. To demonstrate the effectiveness and speed of HKA, we... 

Harmony search (HS) is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments' pitches searching for a perfect state of harmony. A new variant of HS, called global-best harmony search (GHS), is proposed in this paper where concepts from swarm intelligence are borrowed to enhance the performance of HS. The performance of the GHS is investigated and compared with HS and a recently developed variation of HS. The experiments conducted show that the GHS generally outperformed the other approaches when applied to ten benchmark problems. The effect of noise on the performance of the three HS variants is investigated and a... 

In this paper real-time optimization of the Tennessee Eastman challenge process is accomplished in a fuzzy environment. This study focuses on violation of TE constraints in fuzzy environment and also violation of some additional constraints originated from operating mode of the plant. Furthermore the objective function is treated in fuzzy form. Results show a great reduction of operating cost, which is selected as the objective function of this study. The theory of fuzzy optimization, which is the main contribution of this paper, is to reformulate the fuzzy constraints and fuzzy objective function into a crisp programming problem and to solve it by a conventional method of optimization. The... 

This paper is a complete survey of flowshop-scheduling problems and contributions from early works of Johnson of 1954 to recent approaches of metaheuristics of 2004. It mainly considers a flowshop problem with a makespan criterion and it surveys some exact methods (for small size problems), constructive heuristics and developed improving metaheuristic and evolutionary approaches as well as some well-known properties and rules for this problem. Each part has a brief literature review of the contributions and a glimpse of that approach before discussing the implementation for a flowshop problem. Moreover, in the first section, a complete literature review of flowshop-related scheduling... 

The critical issue in designing correlated data networks like Wireless Sensor Networks is to minimize the total cost of data transmission in the network, and decrease the amount of data flow. The problem of finding optimal aggregation tree for correlated data gathering in single sink network is considered as an NP-Complete problem and hence heuristic methods are usually applied to solve it[1]. In this paper, we apply genetic algorithm (GA) to solve the problem. In our method, we improve the performance of genetic search by selecting proper initial population. This initial population is determined in two ways, by using Prime's algorithm, and shortest path tree. The main issue is to regard... 

The facility location problem and its related financial issues have a significant impact on the configuration of a supply chain structure (SCS). Although affording the setup cost of facilities requires a huge financial investment, a meticulous planning to recoup it has been neglected by an overwhelming majority of previous studies. To fill this gap, an economical investment plan of facilities is determined by striking a trade-off between the contribution of the stockholders’ capital and the loan arrangement. In the context of a multiperiod three-echelon SCS, the model also determines the optimum location of plants and the best supply and distribution patterns. The model maximizes the fill... 

In recent years, a number of new heuristic search methods have been developed in the field of automated planning. Enforced hill climbing (EHC) is one such method which has been frequently used in a number of AI planning systems. Despite certain weaknesses, such as getting trapped in dead-ends in some domains, this method is more competitive than several other methods in many planning domains. In order to enhance the efficiency of ordinary enforced hill climbing, a new form of enforced hill climbing, called guided enforced hill climbing, is introduced in this paper. An adaptive branch ordering function is the main feature that guided enforced hill climbing has added to EHC. Guided enforced... 

The cutting stock problem is an important problem in the field of combinational optimization. In classic versions of this problem, the aim is to find a solution for cutting some equal-width mother rolls into smaller parts with given width so that the amount of trim-loss become as small as possible. There are plenty of works which addressed the classic cutting stock problem. However in real world applications there are usually some constraints which make the problem different form classical version and make it harder to solve. In this paper we investigate a special cutting stock problem in film industry and show that it can not be solved by using previous methods. We propose a new hybrid... 

The Burr III distribution properly approximates many familiar distributions such as Normal, Lognormal, Gamma, Weibull, and Exponential distributions. It plays an important role in reliability engineering, statistical quality control, and risk analysis models. The Burr III distribution has four parameters known as location, scale, and two shape parameters. The estimation process of these parameters is controversial. Although the maximum likelihood estimation (MLE) is understood as a straightforward method in parameters estimation, using MLE to estimate the Burr III parameters leads to maximize a complicated function with four unknown variables, where using a conventional optimization such as... 

In this paper, we present an energy-efficient manycast routing and spectrum assignment (EEM-RSA) algorithm in elastic optical networks supporting cloud computing applications. The proposed EEM-RSA is adapted for both static and dynamic scenarios. First, an integer linear programing formulation is derived for energy-efficient manycasting and spectrum assignment; then, the corresponding heuristic methods are proposed. To reduce the energy consumption, inactive (idle) elements are turned off, and in the proposed energy-efficient manycasting heuristic, the number of activated elements are minimized. The power consumption of network elements is modeled by considering a constant overhead for the... 

We investigate the problem of energy-efficient resource allocation constrained to quality of service and physical requirements in orthogonal frequency division multiplexing (OFDM)-based elastic optical networks and propose a fast two-stage algorithm to solve it. The first stage of the proposed algorithm deals with routing, traffic grooming, and traffic ordering and mainly aims at minimization of the number of deployed optical amplifiers and transponders. We provide an integer linear program for routing and traffic grooming and propose a heuristic procedure, which yields its near-optimum solution in a shorter runtime. In the second stage, we optimize transponder parameters to minimize total... 

Sensor network deployment is very challenging due to hostile and unpredictable nature of usage environments. In this letter, we propose two methods for the self-deployment of mobile sensors. The first one is a randomized solution that provides both simplicity and applicability to different environments. Inspired by simulated annealing, it improves both speed and energy conservation of the deployment process. The other method is suggested for environments where sensors form a connected graph, initially. At the cost of this extra limitation, we gain considerable improvements. © 2006 IEEE  

In recently proposed cipher algorithms, power functions over finite fields and specially inversion functions play an important role in the S-box design structure. In this study, a new systematic efficient method is introduced to cryptanalyse (to simplify and approximate) such S-boxes. This method is very simple and does not need any heuristic attempt and can be considered as a quick criterion to find some simple approximations. Using this new method, some approximations can be obtained for advanced encryption standard (AES) like S-boxes, such as AES, Camellia, Shark and so on. Finally as an application of this method, a simple linear approximation for AES S-box is presented. © The... 

This paper proposes a hybrid heuristic optimization method based on genetic algorithm and immune systems to maximize the benefits of Distribution Network Operators (DNOs) accrued due to sizing and placement of Distributed Generation (DG) units in distribution networks. The effects of DG units in reducing the reinforcement costs and active power losses of distribution network have been investigated. In the presented method, the integration of DG units in distribution network is done considering both technical and economical aspects. The strength of the proposed method is evaluated by applying it on a small and a realistic large scale distribution network and the results are compared with...