This paper studies a parallel version of the Vector Evaluated Particle Swarm Optimization (VEPSO) method for multiobjective problems. Experiments on well known and widely used test problems are performed, aiming at investigating both the efficiency of VEPSO as well as the advantages of the parallel implementation. The obtained results are compared with the corresponding results of the Vector Ev...

Potency is a key optimization parameter for hemophilia A gene therapy product candidates. Optimization strategies include promoter engineering to increase transcription, codon optimization of mRNA to improve translation, and amino-acid substitution to promote secretion. Herein, we describe both rational and empirical design approaches to the development of a minimally sized, highly potent AAV-f...

The optimization criterion and a practically feasible new algorithm is stated for the optimization of the index assignments of a multiple description unconstrained vector quantizer with an arbitrary number of descriptions. In the simulations, the index-optimized multiple description vector quantizer achieves significant gains in source SNR over scalar multiple description schemes.

This paper proposes a novel optimization algorithm combined the mechanism of quantum evolutionary algorithm and real-coding method, called an improved quantum particle swarm optimization algorithm (IQPSO). Like the traditional particle swarm optimization, IQPSO is also characterized by position vector and velocity vector to implement the evolution process. However, the particle of IQPSO is divi...

Classification performance of support vector machine (SVM) will be influenced by its model parameters. For this problem, a new method named magnetic bacteria optimization algorithm (MBOA) that optimizes the parameters of SVM is proposed. It is tested by the UCI standard data sets and compared with the other optimization algorithms, such as particle swarm optimization (PSO). Experimental results...

In this article we develop a new approach to duality theory for convex vector optimization problems. We modify a given (set-valued) vector optimization problem such that the image space becomes a complete lattice (a sublattice of the power set of the original image space), where the corresponding infimum and supremum are sets that are related to the set of (minimal and maximal) weakly efficient...

Motivated by applications to the real world, various optimality criteria (also approximate ones) are developed for situations in vector optimization. We propose a new type of solution based on upper comprehensive sets and we discuss the existence of optimal points in multicriteria situations.

We consider two generalized Minty vector variational-like inequalities and investigate the relations between their solutions and vector optimization problems for non-differentiable α-invex functions.