This chapter presents a Discrete Multi-objective Particle Swarm Optimization (MOPSO) algorithm that determines the optimal order of activities execution within a design project that minimizes project total iterative time and cost. Numerical Design Structure Matrix (DSM) was used to model project activities’ execution order along with their interactions providing a base for calculating the objec...

In this paper, the influence of -dominance on Multi-objective Particle Swarm Optimization (MOPSO) methods is studied. The most important role of dominance is to bound the number of non-dominated solutions stored in the archive (archive size), which has influences on computational time, convergence and diversity of solutions. Here, -dominance is compared with the existing clustering technique fo...

Mathematical programming and artificial intelligence (AI) methods are known as the most effective and applicable procedures to form manufacturing cells in designing a cellular manufacturing system (CMS). In this paper, a bi-objective programming model is presented to consider the cell formation problem that is solved by a proposed multi-objective particle swarm optimization (MOPSO). The model c...

Abstract There are many complex multi-objective optimization problems in the real world, which difficult to solve using traditional methods. Multi-objective particle swarm is one of effective algorithms such problems. This paper proposes a with dynamic population size (D-MOPSO), helps compensate for lack convergence and diversity brought by optimization, makes full use existing resources search...

The structural parameters of the magnetorheological (MR) damper significantly affect output damping force and dynamic range. This paper presents a design optimization method to improve performance novel MR with bended magnetic circuit folded flow gap. multiobjective this was carried out based on optimal Latin hypercube (Opt LHD), ellipsoidal basis function neural network (EBFNN), particle swarm...

[1] The operation of large-scale water resources systems often involves several conflicting and noncommensurable objectives. The full characterization of tradeoffs among them is a necessary step to inform and support decisions in the absence of a unique optimal solution. In this context, the common approach is to consider many single objective problems, resulting from different combinations of ...