This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guaran...

Robotics deployed in the underwater medium are subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this context the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be con...

This paper presents Multi-Step A* (MSA*), a search algorithm based on A* for multi-objective 4D vehicle motion planning (three spatial and one time dimension). The research is principally motivated by the need for offline and online motion planning for autonomous Unmanned Aerial Vehicles (UAVs). For UAVs operating in large, dynamic and uncertain 4D environments, the motion plan consists of a se...

In this paper, we study multi-objective temporal planning problems in discrete time and space formulated as single-objective dynamic optimization problems with a minimax objective function. We propose efficient node-dominance relations for pruning states that will not lead to locally optimal plans. Based on the theory of Lagrange multipliers in discrete space, we present the necessary and suffi...

This paper presents a multi-objective transmission expansion planning (TEP) framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component av...

This paper presents a multi-objective order allocation planning problem with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization process, a Monte Carlo simulation technique and a heuristic pruning technique, is proposed to handle this problem. Experiments based on industrial data are condu...

In this paper, we study multi-objective temporal planning problems in discrete time and space formulated as single-objective dynamic optimization problems with a minimax objective function. We propose efficient node-dominance relations for pruning states that will not lead to locally optimal plans. Based on the theory of Lagrange multipliers in discrete space, we present the necessary and suffi...

Automated planning addresses the problem of generating a sequence of actions to satisfy given goal conditions for a constructed model of the world. In recent planning approaches heuristic guidance is used to lead the search towards the goal. The focus of this work is on domains where plan quality is assessed with plan metrics. A discussion of the impact of a popular relaxed planning graph heuri...

Multi-objective AI planning suffers from a lack of benchmarks with known Pareto Fronts. A tunable benchmark generator is proposed, together with a specific solver that provably computes the true Pareto Front of the resulting instances. A wide range of Pareto Front shapes of various difficulty can be obtained by varying the parameters of the generator. The experimental performances of an actual ...

A novel approach for sensor planning, which incorporates multi-objective optimization principals into the autonomous design of sensing strategies, is presented. The study addresses planning the behavior of an automated 3D inspection system, consisting of a manipulator robot in an Eye-on-Hand configuration. Task planning in this context is stated as a constrained multi-objective optimization pro...