Abstract. Conventional multi-objective Particle Swarm Optimization (PSO) algorithms aim to find a representative set of Pareto-optimal solutions from which the user may choose preferred solutions. For this purpose, most multi-objective PSO algorithms employ computationally expensive comparison procedures such as non-dominated sorting. We propose a PSO algorithm, Reference point-based PSO using a Steady-State approach (RPSO-SS), that finds a preferred set of solutions near user-provided reference points, instead of the entire set of Pareto-optimal solutions. RPSO-SS uses simple replacement strategies within a steadystate environment. The efficacy of RPSO-SS in finding desired regions of solutions is illustrated using some well-known two and three-objective test problems. 1

Abstract.- Tuning the parameters of any evolutionary algorithm is a difficult task. In this paper, we describe some experiments done in order to explore the impact of the main parameters of the particle swarm optimization algorithm, when using it for multi-objective optimization. These parameters are the inertia weight and the learning factors involved in the velocity update formula. Also, in our study, we included some parameters from our own multi-objective approach. As a result of our study, we propose three different mechanisms to adapt the values of those parameters that are found to be the most important for the performance of our approach. The mechanisms proposed are validated using seven different test functions taken from the specialized literature of multiobjective optimization. The obtained results show that it is possible to design on-line adaptation mechanisms able to maintain, and even improve, the quality of the obtained solutions, without increasing the computational cost. 1.

Abstract. The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm towards the Pareto optimal front. The proposed algorithm uses a Differential Evolution operator to create the leaders. These leaders can successfully guide the other particles towards the Pareto optimal front for various types of test problems. This simple yet robust algorithm is effective compared with existing multi-objective particle swarm algorithms.

In this paper, we propose a dynamic, non-dominated sorting, multiobjective particle-swarm-based optimizer, named Hierarchical Non-dominated Sorting Particle Swarm Optimizer (H-NSPSO), for memory usage optimization in embedded systems. It significantly reduces the computational complexity of others Multi-Objective Particle Swarm Optimization (MOPSO) algorithms. Concretely, it first uses a fast non-dominated sorting approach with O(mN 2) computational complexity. Second, it maintains an external archive to store a fixed number of non-dominated particles, which is used to drive the particle population towards the best non-dominated set over many iteration steps. Finally, the proposed algorithm separates particles into multi sub-swarms, building several tree networks as the neighborhood topology. H-NSPSO has been made adaptive in nature by allowing its vital parameters (inertia weight and learning factors) to change within iterations. The method is evaluated using two real world examples in embedded applications and compared with existing covering methods.

New multimedia embedded applications are becoming increasingly dynamic. Thus, they cannot only rely on static data allocation, and must employ Dynamically-allocated Data Types (DDTs) to store their data and efficiently use the limited physical resources of embedded devices. However, the optimization of the DDTs for each target embedded system is a very time-consuming process due to the large design space of possible DDTs implementations and selection for the memory hierarchy of each specific embedded device. Thus, new suitable exploration methods for embedded design metrics (memory accesses, usage and power consumption) need to be developed. In this paper we analyze the benefits of two different exploration techniques for