Recently, DISTRIBUTED CONSTRAINT OPTIMIZATION Problems (DCOP) have been drawing a growing body of attention as an important research area in multi agent systems as a large body of real problems can be modeled by them. The primary goal of this research is to design a DISTRIBUTED and effective algorithm to solve DCOP. There are various criteria that measure the efficiency of DCOP algorithms, but the most efficient algorithm for DCOP is the one by which the computation and communication cost is as low as possible and the quality of the solution is high. In this paper, we focus on an approximate DCOP algorithm called DALO (DISTRIBUTED Asynchronous Local OPTIMIZATION). Using the main idea of the DALO algorithm, we propose a new algorithm to solve DCOP, which exhibits two important improvements over the DALO algorithm. First we use a sequential partial approach to select a coefficient of leaders to compute the best assignment for agents by which the computation and communication cost decrease in the whole DCOP. The second improvement is an evolutionary approach by which the computation and communication burden for each agent decreases. We present some empirical evidences that show our algorithm performs better than the DALO algorithm.

In this paper, we focus on the utilization of the feasible value CONSTRAINT technique to address multiobjective OPTIMIZATION problems (MOPs). It is attempted to overcome certain drawbacks associated with this method, such as restrictions on functions and weights, inflexibility in CONSTRAINTs, and challenges in assessing proper efficiency. To accomplish this, we propose an improved version of the feasible value CONSTRAINT technique. Then, by incorporating approximate solutions, we establish connections between $\varepsilon$-(weakly, properly) efficient points in a general MOP and $\epsilon$-optimal solutions to the scalarization problem.

In this paper we consider a fractional OPTIMIZATION problem that minimizes the ratio of two quadratic functions subject to a strictly convex quadratic CONSTRAINT. First using the extension of Charnes-Cooper transformation, an equivalent homogenized quadratic reformulation of the problem is given. Then we show that under certain assumptions, it can be solved to global optimality using semidefinite OPTIMIZATION relaxation in polynomial time.

All bridges designed by kind of structure and built by any type of materials، get burnt out sooner or later. These exhaustions are spread due to several factors such as atmospheric conditions، flood or earthquakes، increased over load more than expected in design phase، design and execution quality، and the type of materials affecting that causes to decrease bridge's useful life if these factors will be ignored. Therefore، bridge maintenance makes to increase its useful life if it happens on time. In this paper، Mazandaran province bridges are studied based on failure situation، faults of each bridges، their condition against traffic load and other effective variables. in order to solve the problem، first، required data have been collected by field study after bridges inspection and then bridges are prioritized based on fault evaluation criteria by Analytic Hierarchy Process (AHP) multi attribute decision making method. Finally، corrective actions are determined based on budget level for bridges. To achieve this goal، a linear mathematical programming model firstly presented and then to solve the large scale problem a genetic algorithm has been developed to find the optimal corrective actions. The proposed algorithm is implemented in MATLAB software and results show that the proposed genetic algorithm have suitable performance to solve the problem. Several budget levels have been considered to assess proposed genetic algorithm validation and changing trend of corrective actions number has been studied. It is seen that amount of effectiveness and total number of repairs are increased by soaring budget level

The primary objective of this paper is to enhance several well-known geometric CONSTRAINT qualifications and necessary optimality conditions for nonsmooth semi-infinite OPTIMIZATION problems (SIPs). We focus on defining novel algebraic Mangasarian-Fromovitz type CONSTRAINT qualifications, and on presenting two Karush-Kuhn-Tucker type necessary optimality conditions for nonsmooth SIPs defined by locally Lipschitz functions. Then, by employing a new type of generalized invex functions, we present sufficient conditions for the optimality of a feasible point of the considered problems. It is noteworthy that the new class of invex functions we considered encompasses several classes of invex functions introduced previously. Our results are based on the Michel-Penot subdifferential.

In the last few decades there has been lots of discussion in the literature regarding robust OPTIMIZATION. Since Epsilon CONSTRAINT is one of the most important technique in interactive problems, therefore in this paper, due to the importance of robust OPTIMIZATION and multi-objective programming problems, we consider Multi-Objective Linear Fractional Programming (MOLFP) problem in the presence of box-uncertainty in the coefficients of the objective functions. We propose an approach based on ε,-CONSTRAINT and Charnes-Cooper methods to obtain weakly robust efficient solutions, that have special importance in the literature, for a MOLFP problems in the presence of uncertain data. Charnes-cooper method is applied to reduce a fractional programm to a non fractional programm. At the end we write the robust counterpart of the UMOLFP model in the presence of the box-uncertainty and it's equivalent linear programming problem: Finally a numerical example is used to show the usefulness of the proposed approach.

Resource CONSTRAINTs Project Scheduling Problem (RSPSP) seeks proper sequence of implementation of project activities in a way that the precedence relations and different type of resource CONSTRAINTs are met concurrently. RCPSP tends to optimize some measurement function as make-span, cost of implementation, number of tardy tasks and etc. As RCPSP is assumed as an NP-Hard problem so, different meta-heuristic approaches have been proposed to solve different variants of it. In this paper, a modified Ant Colony OPTIMIZATION (ACO) approach has been developed to deal with RCPSP. The definition of probabilistic selection rule has been modified in proposed approach in favor of better performance. Moreover, the parameters of algorithm have been determined in an adaptive manner and the stagnation behavior has been prevented in high iterations of algorithm. Uncertainty of parameters of RCPSP has also been discussed. The proposed algorithm has been coded using Visual Basic software and tested on benchmark instance in this area. The results are promising and have been compared with optimal or best known solutions.

The purpose of this paper is to survey of Muslim consumer behavior with respect to a given portfolio. In other words, in this paper, a set of specified assets with returns in certain state be considered as the individual's budget CONSTRAINT. In this regard, factors affecting the portfolio selection by the individual in Islamic economics will be discussed. Also the subject of consumption in Islamic Economics is analyzed and finally the principal model of paper will be provided. For this purpose a Muslim, utility of consumption and the utility of Infaq in the current period and future (future periods discounted with the rate of time preference) and maximize with respect of his portfolio. Finally will be specified that the value of Asset in certain state with marginal utility of Infaq and consumption in current time have inverse relationship and marginal utility of Infaq and consumption in future time, time preference of investor– consumer and the probability of a given situation related to a specific asset have a direct relationship. It also use of this model can be derived the function of consumption and Infaq.