task assignment problem (tap) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. tap is a combinatorial optimization problem and np-complete. this paper proposes a hybrid meta-heuristic algorithm for solving tap in a h...

In this paper, a new version of multi-objective differential evolution with dynamically adaptable mutation factor is used for Pareto optimization of a 5-degree of freedom vehicle vibration model excited by non-stationary random road profile. In this way, non-dominated sorting algorithm and crowding distance criterion have been combined to differential evolution with fuzzified mutation in order ...

Selecting approaches with appropriate accuracy and suitable speed for the purpose of making decision is one of the managers’ challenges. Also investing decision is one of the main decisions of managers and it can be referred to securities transaction in financial markets which is one of the investments approaches. When some assets and barriers of real world have been considered, optimization of...

This paper deals with resource unconstrained project scheduling problems with the objective of maximizing the net present value (NPV) of project cash flows. Here we present a heuristic algorithm named as differential procedure (Dif_AOA). In order to evaluate the efficiency of this algorithm, networks with node numbers between 10-1000 and network complexity coefficients between 1.3-6.6 have be...

Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using classical optimization methods, this paper presents a Multi-Objective Bees Algorithm (MOBA) for solving the multi-objective optimal of mechanical engineering problems design. In the pre...

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cann...