Sharif Digital Repository

Scheduling sports leagues has drawn significant attention to itself in recent years, as it involves considerable revenue as well as challenging combinatorial optimization problems. A particular class of these problems is the Traveling Tournament Problem (TTP) which focuses on minimizing the total traveling distance for teams. In this paper, an efficient simulated annealing approach is presented for TTP which applies two simultaneous and disparate models for the problem in order to search the solutions space more effectively. Also, a computationally efficient modified greedy scheme is proposed for constructing a favorable initial solution for the simulated annealing algorithm. Our... 

Previous researches have shown the success of using reinforcement learning in solving combinatorial optimization problems. The main idea of these methods is to learn (near) optimal evaluation function to improve local searches and find (near) optimal solutions. Stage algorithm introduced by Boyan & Moore, is one of the most important algorithm in this area. In the other hand fuzzy methods have been used in all fields of science to solve problems but still never used in combinatorial optimization problems. In this paper we focus on Bin Packing Problem. We introduce two basic fuzzy algorithms (ALM and IDS) and then solve our problem with these fuzzy algorithms. We run ALM and IDS algorithms on... 

One of the challenging problems in artificial intelligence is credit assignment which simply means distributing the credit among a group, such as a group of agents. We made an attempt to meet this problem with the aid of the reinforcement learning paradigm. In this paper, expertness framework is defined and applied to the multi-agent credit assignment problem. In the expertness framework, the critic agent, who is responsible for distributing credit among agents, is equipped with learning capability, and the proposed credit assignment solution is based on the critic to learn to assign a proportion of the credit to each agent, and the used proportion should be learned by reinforcement... 

Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations hopefully producing an offspring that has the good characteristics of both parents and this requires explicit chromosome and recombination operator design. This paper presents a novel evolutionary approach based on symbiogenesis which uses symbiotic combination instead of sexual recombination and using this operator, it requires no domain knowledge for chromosome or combination operator design. The algorithm is benchmarked on three problem sets, combinatorial optimization, deceptive, and fully deceptive, and is compared with standard... 

Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations hopefully producing an offspring that has the good characteristics of both parents. Symbiotic Combination is formerly introduced as an alternative for sexual recombination operator to overcome the need of explicit design of recombination operators in GA all. This paper presents an optimization algorithm based on using this operator in Tabu Search. The algorithm is benchmarked on two problem sets and is compared with standard genetic algorithm and symbiotic evolutionary adaptation model, showing success rates higher than both cited... 

The job shop scheduling problem is a difficult combinatorial optimization problem. This paper presents a hybrid algorithm which combines global equilibrium search, path relinking and tabu search to solve the job shop scheduling problem. The proposed algorithm used biased random sampling to have a better covering of the solution space. In addition, a new version of N6 neighborhood is applied in a tabu search framework. In order to evaluate the algorithm, comprehensive tests are applied to it using various standard benchmark sets. Computational results confirm the effectiveness of the algorithm and its high speed. Besides, 19 new upper bounds among the unsolved problems are found  

Many genetic algorithms (GA) have been applied to solve different NP-complete combinatorial optimization problems so far. The striking point of using GA refers to selecting a combination of appropriate patterns in crossover, mutation, and and so forth and fine tuning of some parameters such as crossover probability, mutation probability, and and so forth. One way to design a robust GA is to select an optimal pattern and then to search for its parameter values using a tuning procedure. This paper addresses a methodology to both optimal pattern selection and the tuning phases by taking advantage of design of experiments and response surface methodology. To show the performances of the proposed... 

Edit distance and longest common subsequence are among the most fundamental problems in combinatorial optimization. Recent developments have proven strong lower bounds against subquadratic time solutions for both problems. Moreover, the best approximation factors for subquadratic time solutions have been limited to 3 for edit distance and super constant for longest common subsequence. Improved approximation algorithms for these problems1 are some of the biggest open questions in combinatorial optimization. In this work, we present improved algorithms for both edit distance and longest common subsequence. The running times are truly subquadratic, though we obtain 1 + o(1) approximate... 

Edit distance is one of the most fundamental problems in combinatorial optimization to measure the similarity between strings. Ulam distance is a special case of edit distance where no character is allowed to appear more than once in a string. Recent developments have been very fruitful for obtaining fast and parallel algorithms for both edit distance and Ulam distance. In this work, we present an almost optimal MPC (massively parallel computation) algorithm for Ulam distance and improve MPC algorithms for edit distance. Our algorithm for Ulam distance is almost optimal in the sense that (1) the approximation factor of our algorithm is $1+epsilon$1+ϵ, (2) the round complexity of our... 

We study the problem of fair allocation for indivisible goods. We use the maximin share paradigm introduced by Budish [Budish E (2011) The combinatorial assignment problem: Approximate competitive equilibrium from equal incomes. J. Political Econom. 119(6):1061-1103.] as a measure of fairness. Kurokawa et al. [Kurokawa D, Procaccia AD, Wang J (2018) Fair enough: Guaranteeing approximate maximin shares. J. ACM 65(2):8.] were the first to investigate this fundamental problem in the additive setting. They showed that in delicately constructed examples, not everyone can obtain a utility of at least her maximin value. They mitigated this impossibility result with a beautiful observation: no... 

Ant Colony Optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperate in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to Max-Min Ant System structure and exploits a local search heuristic to improve its performance. Experimental results on DIMACS test instances show improvements over existing ACO algorithms of the graph coloring problem  

We consider the problem of walking in an unknown street, starting from a point s, to reach a target t by a robot which has a minimal sensing capability. The goal is to decrease the traversed path as short as possible. The robot cannot infer any geometric properties of the environment such as coordinates, angles or distances. The robot is equipped with a sensor that can only detect the discontinuities in the depth information (gaps) and can locate the target point as soon as it enters in its visibility region. In addition, a pebble as an identifiable point is available to the robot to mark some position of the street. We offer a data structure similar to Gap Navigation Tree to maintain the... 

In the Touring Polygons Problem (TPP) there is a start point s, a sequence of simple polygons P = (P1,...,Pk) and a target point t in the plane. The goal is to obtain a path of minimum possible length that starts from s, visits in order each of the polygons in P and ends at t. This problem has a polynomial time algorithm when the polygons in P are convex and is NP-hard in general case. But, it has been open whether the problem is NP-hard when the polygons are pairwise disjoint. In this paper, we prove that TPP is also NP-hard when the polygons are pairwise disjoint in any Lp norm even if each polygon consists of at most two line segments. This result solves an open problem from STOC '03 and... 

Most of the publications regarding the maxim covering location problem (MCLP) address the case where the decision is to be made for one period. In this paper, we deal with a rather untouched version of MCLP which is called dynamic MCLP (DMCLP). In order to solve this problem, a simulated annealing (SA) has been presented. The proposed solution algorithm is capable of solving problems with up to 2500 demand nodes and 200 potential facilities with a fair amount of exactness. Our experiments showed that the proposed approach finds solutions with errors less than one percent  

In the touring polygons problem (TPP), for a given sequence (s= P0, P1, ⋯, Pk, t = Pk+1) of polygons in the plane, where s and t are two points, the goal is to find a shortest path that starts from s, visits each of the polygons in order and ends at t. In the constrained version of TPP, there is another sequence (F0, ⋯, Fk) of polygons called fences, and the portion of the path from Pi to Pi+1 must lie inside the fence Fi. TPP is NP-hard for disjoint non-convex polygons, while TPP and constrained TPP are polynomially solvable when the polygons are convex and the fences are simple polygons. In this work, we present the first polynomial time algorithm for solving constrained TPP when the... 

In this paper, a combined model of auto-transit assignment is introduced based on two complementarity formulations in the literature. The model accounts for interactions between the auto and transit modes through non-separable asymmetric demand and cost functions. A path-based solution algorithm is presented based on the three ideas of decomposition, column generation, and linearization, which have proved to be effective in tackling large-size networks. Numerical results over the Chicago sketch network suggest that the algorithm converges quickly within the first iterations, but is less effective as the solution gets closer to the neighborhood of the equilibrium solution. The sluggish... 

The optimal reactive power dispatch (ORPD) problem is formulated as a combinatorial optimization problem involving nonlinear objective Junction with multiple local minima. In this paper, as a new approach, different ant colony optimization (ACO) algorithms are applied to the reactive power dispatch problem. Ant system (AS), the firstly introduced ant colony optimization algorithm, and its direct successors, elitist ant system (EAS), rank-based ant system (ASrank) and max-min ant system (MMAS), are employed to solve the reactive power dispatch problem. To analyze the efficiency and effectiveness of these modern search algorithms, the proposed methods are applied to the IEEE 30-bus system and...