Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters

Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP into multiple single-optimization sub-problems, achieving better diversity and a better approximation of the Pareto front, and dealing with some of the challenges of MaOPs. However, these approaches still require one to solve a multi-criteria selection problem that will allow a Decision-Maker (DM) to choose the final solution. Incorporating preferences may provide results that are closer to the region of interest of a DM. Most of the proposals to integrate preferences in decomposition-based MOEAs prefer progressive articulation over the “a priori” incorporation of preferences. Progressive articulation methods can hardly work without comparable and transitive preferences, and they can significantly increase the cognitive effort required of a DM. On the other hand, the “a priori” strategies do not demand transitive judgements from the DM but require a direct parameter elicitation that usually is subject to imprecision. Outranking approaches have properties that allow them to suitably handle non-transitive preferences, veto conditions, and incomparability, which are typical characteristics of many real DMs. This paper explores how to incorporate DM preferences into MOEA/D using the “a priori” incorporation of preferences, based on interval outranking relations, to handle imprecision when preference parameters are elicited. Several experiments make it possible to analyze the proposal's performance on benchmark problems and to compare the results with the classic MOEA/D without preference incorporation and with a recent, state-of-the-art preference-based decomposition algorithm. In many instances, our results are closer to the Region of Interest, particularly when the number of objectives increases

Wind Energy and Multicriteria Analysis in Making Decisions on the Location of Wind Farms: A Case Study in the North-Eastern of Poland

This chapter presents an investigation of different methods of multicriteria analysis and different rules of proceedings that have to be taken into account for making decision about location of a wind farm with application in the north-eastern (NE) Poland. Ten multicriteria analyses were discussed taking into account the main criteria on which they are based on utility functions (MAUT, AHP, and DEMATEL), relationship outranking (ELECTRE, PROMETHEE, and ARROW-RAYNAUD), distances (TOPSIS), and decision support (BORDA ranking methods and their modified and COPELAND). Taking into account of nine criteria that should be met by the location of 15 wind turbines in Krynki and Szudzialowo communities, the main three criteria (C3, C8, and C9) were found to differentiate location of eight wind turbines (T-6–T-13), according to two variants (I and II). The Borda ranking method proved that from among the two variants considered, the more suitable location of wind turbines is second variant W II than first variant W I. Variant W II had a higher altitude of the terrain (C3) and less risk of impact on birds (C8) and bats species (C9) than variant W I

Multi-objective Path Finding Using Reinforcement Learning

Path Finding is a vastly studied subject in the field of Computer Science. The problem of path-finding is defined as the discovery and plotting of an optimal route between two points on a plane. The existing algorithms that solve this problem are mostly static and rely heavily on the prior knowledge of the environment. They also require the environment to be deterministic. However, in real-world applications of the path-finding problem, often the environment is priorly unknown and stochastic, and with several conflicting objectives. In such cases, the aforementioned algorithms fail to produce effective results. In this project, we study and use a reinforcement learning approach for solving the many-objective path-finding problem, called Voting Q-Learning (VoQL), a model-free, on-policy learning algorithm. In this project, a set of optimal policies is determined with the help of the VoQL algorithm. This algorithm uses various voting methods borrowed from the field of social choice theory for action-selection. In addition to working with the existing methods for VOQL, the performance of additional voting methods is studied and evaluated for the first time

Mobile robotic network deployment for intruder detection and tracking

This thesis investigates the problem of intruder detection and tracking using mobile robotic networks. In the first part of the thesis, we consider the problem of seeking an electromagnetic source using a team of robots that measure the local intensity of the emitted signal. We propose a planner for a team of robots based on Particle Swarm Optimization (PSO) which is a population based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this thesis. Our results are also validated on an experimental testbed. In the second part, we present a game theoretic framework for visibility-based target tracking in multi-robot teams. A team of observers (pursuers) and a team of targets (evaders) are present in an environment with obstacles. The objective of the team of observers is to track the team of targets for the maximum possible time. While the objective of the team of targets is to escape (break line-of-sight) in the minimum time. We decompose the problem into two layers. At the upper level, each pursuer is allocated to an evader through a minimum cost allocation strategy based on the risk of each evader, thereby, decomposing the agents into multiple single pursuer-single evader pairs. Two decentralized allocation strategies are proposed and implemented in this thesis. At the lower level, each pursuer computes its strategy based on the results of the single pursuer-single evader target-tracking problem. We initially address this problem in an environment containing a semi-infinite obstacle with one corner. The pursuer\u27s optimal tracking strategy is obtained regardless of the evader\u27s strategy using techniques from optimal control theory and differential games. Next, we extend the result to an environment containing multiple polygonal obstacles. We construct a pursuit field to provide a guiding vector for the pursuer which is a weighted sum of several component vectors. The performance of different combinations of component vectors is investigated. Finally, we extend our work to address the case when the obstacles are not polygonal, and the observers have constraints in motion

Automatically designing more general mutation operators of evolutionary programming for groups of function classes using a hyper-heuristic

In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the function set. The mutation operators are automatically designed for a specific function class. The contribution of this paper is to show that a GP can not only automatically design a mutation operator for Evolutionary Programming (EP) on functions generated from a specific function class, but also can design more general mutation operators on functions generated from groups of function classes. In addition, the automatically designed mutation operators also show good performance on new functions generated from a specific function class or a group of function classes

Designing multiple classifier combinations a survey

Classification accuracy can be improved through multiple classifier approach. It has been proven that multiple classifier combinations can successfully obtain better classification accuracy than using a single classifier. There are two main problems in designing a multiple classifier combination which are determining the classifier ensemble and combiner construction. This paper reviews approaches in constructing the classifier ensemble and combiner. For each approach, methods have been reviewed and their advantages and disadvantages have been highlighted. A random strategy and majority voting are the most commonly used to construct the ensemble and combiner, respectively. The results presented in this review are expected to be a road map in designing multiple classifier combinations

Evolutionary Dynamic Multi-Objective Optimisation : A survey

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