| Many, if not most, optimization problems have multiple objectives. Historically , multiple objectives have been combined ad hoc to form a scalar objective function , usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modiied to deal with multiple objectives by incorporating ...

Many of the standard optimization algorithms focus on optimizing a single, scalar feedback signal. However, real-life optimization problems often require a simultaneous optimization of more than one objective. In this paper, we propose a multi-objective extension to the standard X -armed bandit problem. As the feedback signal is now vector-valued, the goal of the agent is to sample actions in t...

mance of multiple objective evolutionary algorithms on distribution system desi gn problem – computational experiment, in: Abstract. The paper presents a comparative experiment with fo ur multiple objective evolutionary algorithms on a real life co mbinatorial optimization problem. The test problem corresponds to the design of a distribution system. The experiment compares performance of a Pare...

Innovization (innovation through optimization) is a relatively new concept in the field of multi-objective engineering design optimization. It involves the use of the Pareto-optimal solutions of a problem, to unveil hidden mathematical relationships between variables, objectives and constraint functions. Since the relationships themselves come from the Pareto-optimal dataset, they can be though...

Surrogate-based optimization has proven very useful for novel or exploratory design tasks because it offers a global view of the characteristics of the design space, and it enables one to refine the design of experiments, conduct sensitivity analyses, characterize tradeoffs between multiple objectives, and, if necessary, help modify the design space. In this article, a framework is presented fo...

In most real world multi-objective optimization problems the objectives are conflicting and therefore, they do not lend themselves to a single solution but result in a set of non-dominating solutions. Several issues arise in Multi-objective Optimization. Firstly, the entire search space has to be searched (in order to find all the good nondominated solutions) without getting stuck in local opti...

Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the extended multi-objective search-space. We will demonstrate that the powerful quantum-assisted dynamic programming optimization framework is capable of circu...

the aim of this paper is to present an efficient method for a rail freight car fleet sizing problem. this problem is modeled mathematically as a multi-period, dynamic and multi-objective, in which the rail freight wagons are assumed to be heterogeneous. demands for different wagons and all travel times are assumed deterministic. in order to increase the utilization of the available wagons in th...