Multi agent collaborative search based on Tchebycheff decomposition

This paper presents a novel formulation of Multi Agent Collaborative Search, for multi-objective optimization, based on Tchebycheff decomposition. A population of agents combines heuristics that aim at exploring the search space both globally (social moves) and in a neighborhood of each agent (individualistic moves). In this novel formulation the selection process is based on a combination of Tchebycheff scalarization and Pareto dominance. Furthermore, while in the previous implementation, social actions were applied to the whole population of agents and individualistic actions only to an elite sub-population, in this novel formulation this mechanism is inverted. The novel agent-based algorithm is tested at first on a standard benchmark of difficult problems and then on two specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi objective optimization algorithms. The results demonstrate that this novel agent-based search has better performance with respect to its predecessor in a number of cases and converges better than the other state-of-the-art algorithms with a better spreading of the solutions

Improved archiving and search strategies for multi agent collaborative search

This paper presents a new archiving strategy and some modified search heuristics for the Multi Agent Collaborative Search algorithm (MACS). MACS is a memetic scheme for multi-objective optimisation that combines the local exploration of the neighbourhood of some virtual agents with social actions to advance towards the Pareto front. The new archiving strategy is based on the physical concept of minimising the potential energy of a cloud of points each of which repels the others. Social actions have been modified to better exploit the information in the archive and local actions dynamically adapt the maximum number of coordinates explored in the pattern search heuristic. The impact of these modifications is tested on a standard benchmark and the results are compared against MOEA/D and a previous version of MACS. Finally, a real space related problem is tackled

A direct memetic approach to the solution of multi-objective optimal control problems

This paper proposes a memetic direct transcription algorithm to solve Multi-Objective Optimal Control Problems (MOOCP). The MOOCP is first transcribed into a Non-linear Programming Problem (NLP) with Direct Finite Elements in Time (DFET) and then solved with a particular formulation of the Multi Agent Collaborative Search (MACS) framework. Multi Agent Collaborative Search is a memetic algorithm in which a population of agents combines local search heuristics, exploring the neighbourhood of each agent, with social actions exchanging information among agents. A collection of all Pareto optimal solutions is maintained in an archive that evolves towards the Pareto set. In the approach proposed in this paper, individualistic actions run a local search, from random points within the neighbourhood of each agent, solving a normalised Pascoletti-Serafini scalarisation of the multi-objective NLP problem. Social actions, instead, solve a bi-level problem in which the lower level handles only the constraint equations while the upper level handles only the objective functions. The proposed approach is tested on the multi-objective extensions of two well-known optimal control problems: the Goddard Rocket problem, and the maximum energy orbit rise problem

Multi-objective optimisation of many-revolution, low-thrust orbit raising for Destiny mission

This work will present a Multi-Objective approach to the design of the initial, Low-Thrust orbit raising phase for JAXA’s proposed technology demonstrator mission DESTINY. The proposed approach includes a simplified model for Low Thrust, many-revolution transfers, based on an analytical orbital averaging technique, and a simplified control parameterisation. Eclipses and J2 perturbation are also accounted for. This is combined with a stochastic optimisation algorithm to solve optimisation problems in which conflicting performance figures of DESTINY’s trajectory design are concurrently optimised. It will be shown that the proposed approach provides for a good preliminary investigation of the launch window and helps identifying critical issues to be addressed in future design phases

Coordination mechanisms with mathematical programming models for decentralized decision-making, a literature review

[EN] The increase in the complexity of supply chains requires greater efforts to align the activities of all its members in order to improve the creation of value of their products or services offered to customers. In general, the information is asymmetric; each member has its own objective and limitations that may be in conflict with other members. Operations managements face the challenge of coordinating activities in such a way that the supply chain as a whole remains competitive, while each member improves by cooperating. This document aims to offer a systematic review of the collaborative planning in the last decade on the mechanisms of coordination in mathematical programming models that allow us to position existing concepts and identify areas where more research is needed.Rius-Sorolla, G.; Maheut, J.; Estelles Miguel, S.; García Sabater, JP. (2020). Coordination mechanisms with mathematical programming models for decentralized decision-making, a literature review. 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Multi-objective optimisation of low-thrust trajectories

This research work developed an innovative computational approach to the preliminary design of low-thrust trajectories optimising multiple mission criteria. Low-Thrust (LT) propulsion has become the propulsion system of choice for a number of near Earth and interplanetary missions. Consequently, in the last two decades a good wealth of research has been devoted to the development of computational method to design low-thrust trajectories. Most of the techniques, however, minimise or maximise a single figure of merit under a set of design constraints. Less effort has been devoted to the development of efficient methods for the minimisation (or maximisation) of two or more figures of merit. On the other hand, in the preliminary mission design phase, the decision maker is interested in analysing as many design solutions as possible against different trade-off criteria. Therefore, in this PhD work, an innovative Multi-Objective (MO), memetic optimisation algorithm, called Multi-Agent Collaborative Search (MACS2), has been implemented to tackle low-thrust trajectory design problems with multiple figures of merit. Tests on both academic and real-world problems showed that the proposed MACS2 paradigm performs better than or as well as other state-of-the-art Multi-Objective optimisation algorithms. Concurrently, a set of novel approximated, first-order, analytical formulae has been developed, to obtain a fast but reliable estimation of the main trade-off criteria. These formulae allow for a fast propagation of the orbital motion under a constant perturbing acceleration. These formulae have been shown to allow for the fast and relatively accurate propagation of long LT trajectories under the typical acceleration level delivered by current engine technology. Various applications are presented to demonstrate the validity of the combination of the analytical formulae with MACS2. Among them, the preliminary design of the JAXA low-cost DESTINY mission to L2, a novel approach to the optimisation under uncertainty of deflection actions for Near Earth Objects (NEO), and the de-orbiting of space debris with low-thrust and with a combination of low-thrust and solar radiation pressure

New heuristics for multi-objective worst-case optimization in evidence-based robust design

This paper presents a non-nested algorithm for the solution of multi-objective min-max problems (MOMMP) in worst-case optimization. The algorithm has been devised for evidence-based robust optimization, where the lack of a defined probabilistic behaviour of the uncertain parameters makes it impossible to apply sample-based techniques and forces the designer to identify the worst case over the subdomains of the uncertainty space. In evidence theory, the robustness of the solutions is measured in terms of the Belief in the realization of the value of the design budgets, which acts as a lower bound to the unknown cumulative distribution function of the budget. Thus a means of finding robust solutions in preliminary design consists on applying the minimax model, where the worst-case budget over the uncertainty space is optimized over the control space. The paper proposes a novel heuristic to solve MOMMP and demonstrates its capability to approximate the worst-case Pareto front at a very reduced cost with respect to approaches based on nested optimization

A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular multi-objective optimization approach is investigated for the advanced optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades (MICINN)–Agencia Estatal de Investigación (AEI), and by the European Regional Development Funds (ERDF), a way of making Europe, under Grant PGC2018-098946-B-I00 funded by MCIN/AEI/10.13039/501100011033/.Peer ReviewedPostprint (published version

Problem Specific MOEA/D for Barrier Coverage with Wireless Sensors

Barrier coverage with wireless sensors aims at detecting intruders who attempt to cross a specific area, where wireless sensors are distributed remotely at random. This paper considers limited-power sensors with adjustable ranges deployed along a linear domain to form a barrier to detect intruding incidents. We introduce three objectives to minimize: 1) total power consumption while satisfying full coverage; 2) the number of active sensors to improve the reliability; and 3) the active sensor nodes' maximum sensing range to maintain fairness. We refer to the problem as the tradeoff barrier coverage (TBC) problem. With the aim of obtaining a better tradeoff among the three objectives, we present a multiobjective optimization framework based on multiobjective evolutionary algorithm (MOEA)/D, which is called problem specific MOEA/D (PS-MOEA/D). Specifically, we define a 2-tuple encoding scheme and introduce a cover-shrink algorithm to produce feasible and relatively optimal solutions. Subsequently, we incorporate problem-specific knowledge into local search, which allows search procedures for neighboring subproblems collaborate each other. By considering the problem characteristics, we analyze the complexity and incorporate a strategy of computational resource allocation into our algorithm. We validate our approach by comparing with four competitors through several most-used metrics. The experimental results demonstrate that PS-MOEA/D is effective and outperforms the four competitors in all the cases, which indicates that our approach is promising in dealing with TBC