Abstract Multi-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective problems. This is mainly because of the imbalance between convergence and diversity that occurs increasing selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution method developed to improve performance MOPSO. First, new glob...

Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied different design problems, these exhibit deficiencies when solving complex problems with multimodal discontinuous objective functions, which is quite common optimization de...

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...

In civil engineering, structural optimization seeks an efficient use of material resources and the automatization design process a wide range structures such as frames, bridges, other systems. This work develops novel multiobjective topology to minimize planar trusses' weight strain energy. initial stage, optimized discrete geometry ground structure is generated from continuum space with genera...

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 ...