An Adaptive Scheme to Generate the Pareto Front Based on the Epsilon-Constraint Method

We discuss methods for generating or approximating the Pareto set of multiobjective optimization problems by solving a sequence of constrained single-objective problems. The necessity of determining the constraint value a priori is shown to be a serious drawback of the original epsilon-constraint method. We therefore propose a new, adaptive scheme to generate appropriate constraint values during the run. A simple example problem is presented, where the running time (measured by the number of constrained single-objective sub-problems to be solved) of the original epsilon-constraint method is exponential in the problem size (number of decision variables), although the size of the Pareto set grows only linearly. We prove that --- independent of the problem or the problem size --- the time complexity of the new scheme is O(k^{m-1}), where k is the number of Pareto-optimal solutions to be found and m the number of objectives. Simulation results for the example problem as well as for different instances of the multiobjective knapsack problem demonstrate the behavior of the method, and links to reference implementations are provided

Multi-objective evolutionary algorithms for the risk-return trade-off in bank loan management

Abstract Multi-Criteria Decision Making is an increasingly accepted tool for decision making in management. In this work, we highlight the application of a novel Multi-Objective Evolutionary Algorithm, NSGA-II to the risk-return trade-off for a bank loan portfolio manager. The manager of a bank operating in a competitive environment faces the standard goal of maximizing shareholder wealth. Specifically, this attempts to maximize the net worth of the bank, which in turn involves maximizing the net interest margin of the bank (among other factors, such as non-interest income). At the same time, there are significant regulatory constraints placed on the bank, such as the maintenance of adequate capital, interest-rate risk exposure, etc. The Genetic Algorithm based technique used here obtains an approximation to the set of Pareto-optimal solutions which increases the decision flexibility available to the bank manager and provides a visualization tool for one of the tradeoffs involved. The algorithm is also computationally efficient and is contrasted with a traditional multi-objective function -the epsilon-constraint method

Analysis of Multi-Objective Evolutionary Algorithms to Optimize Dynamic Data Types in Embedded Systems

New multimedia embedded applications are increasingly dynamic, and rely on Dynamically-allocated Data Types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large design space of possible DDTs implementations. Thus, suitable exploration methods for embedded design metrics (memory accesses, memory usage and power consumption) need to be developed. In this work we present a detailed analysis of the characteristics of different types of Multi-Objective Evolutionary Algorithms (MOEAs) to tackle the optimization of DDTs in multimedia applications and compare them with other state-of-the-art heuristics. Our results with state-of-the-art MOEAs in two object-oriented multimedia embedded applications show that more sophisticated MOEAs (SPEA2 and NSGA-II) offer better solutions than simple schemes (VEGA). Moreover, the suitable sophisticated scheme varies according to the available exploration time, namely, NSGA-II outperforms SPEA2 in the first set of solutions (300-500 generations), while SPEA2 offers better solutions afterwards

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Platoon Route Optimization for Picking up Automated Vehicles in an Urban Network

International audienceIn this paper, we consider the problem of vehicle collection assisted by a fleet manager where parked vehicles are collected and guided by fleet managers. Each platoon follows a calculated and optimized route to collect and guide the parked vehicles to their final destinations. The Platoon Route Optimization for Picking up Automated Vehicles problem, called PROPAV, consists in minimizing the collection duration, the number of platoons and the total energy required by the platoon leaders. We propose a formal definition of PROPAV as an integer linear programming problem, and then we show how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II), to deal with this multi-criteria optimization problem. Results in various configurations are presented to demonstrate the capabilities of NSGA-II to provide well-distributed Pareto-front solutions

A Multi-Objectif Genetic Algorithm-Based Adaptive Weighted Clustering Protocol in VANET

International audience—Vehicular Ad hoc NETwork (VANET) is the main component that is used recently for the development of Intelligent Transportation Systems (ITSs). VANET has a highly dynamic and portioned network topology due to the constant and rapid movement of vehicles. Recently, the clustering algorithms are widely used as the control schemes to make VANET topology less dynamic for MAC, routing and security protocols. An efficient clustering algorithm must take into consideration all the necessary information related to node mobility. In this paper, we propose an Adaptive Weighted Clustering Protocol (AWCP), specially designed for vehicular networks, which takes the highway ID, direction of vehicles, position, speed and the number of neighbors vehicles into account in order to enhance the network topology stability. However, the multiple control parameters of our AWCP, make parameter tuning a non-trivial problem. In order to optimize AWCP protocol, we define a multi-objective problem whose inputs are the AWCPs parameters and whose objectives are: providing stable cluster structure as possible, maximizing data delivery rate, and reducing the clustering overhead. We then face this multi-objective problem with the the Multi-Objective Genetic Algorithm (MOGA). We evaluate and compare its performance with other multi-objective optimization techniques: Multi-objective Particle Swarm Optimization (MOPSO) and Multi-objective Differential Evolution (MODE). The experiments analysis reveal that NSGA-II improves the results of MOPSO and MODE in terms of the spacing, spread, and ratio of non-dominated solutions and generational distance metrics used for comparison

Robust Optimization of a Post-combustion CO2 Capture Absorber Column under Process Uncertainty

In recent years, greenhouse gas (GHG) emissions is a global concern due to high concentrations of these gases in the atmosphere. Carbon capture and storage (CCS) has been suggested as an attractive alternative to curb intensive CO2 emissions and reduce its impact to the environment. CCS technologies provide a direct alternative to reducing the emissions from coal and gas-fired power generation plants. However, in order to implement commercial-scale CO2 capture plants, further studies are needed to mitigate all possible costs of this technology such as high energy consumption. This work presents a study on a robust design optimization framework for a pilot-scale absorber column in post-combustion CO2 capture. A mechanistic model describing the behaviour of a post-combustion CO2 absorber column is explicitly considered. The proposed formulation takes into account uncertainty that will impact the absorber column due to seasonal or unexpected changes in the operating policies of a fossil-fired power plant, e.g., changes in the flue gas stream, as well as uncertainty associated with the physical thermodynamic properties of the species involved in the absorption process. Furthermore, in addition to the presence of model uncertainty, a multi-objective optimization in a multi-period scenario explicitly describing year-long seasonal changes in flue gas has been considered. Different scenarios were assessed in order to evaluate the impact of uncertainty and multi-period changes on the optimal multi-objective process design. Optimal design specifications between different number of uncertain realizations and periodical changes were studied. However, higher computational demands were observed under extensive evaluations of uncertainty. Results from this study suggest that larger dimensions in design are required when the optimization was evaluated under uncertainty and under multi-periods scenarios considering uncertainty. The results show that the optimal design considering uncertainty and seasonal changes will be able to comply with the CO2 capture policies. Thus, post-combustion CO2 capture systems must be designed under these conditions to ensure feasibility of these plants during operation

Methodology and Software for Interactive Decision Support

These Proceedings report the scientific results of an International Workshop on "Methodology and Software for Interactive Decision Support" organized jointly by the System and Decision Sciences Program of IIASA and The National Committee for Applied Systems Analysis and Management in Bulgaria. Several other Bulgarian institutions sponsored the workshop -- The Committee for Science to the Council of Ministers, The State Committee for Research and Technology and The Bulgarian Industrial Association. The workshop was held in Albena, on the Black Sea Coast. In the first section, "Theory and Algorithms for Multiple Criteria Optimization," new theoretical developments in multiple criteria optimization are presented. In the second section, "Theory, Methodology and Software for Decision Support Systems," the principles of building decision support systems are presented as well as software tools constituting the building components of such systems. Moreover, several papers are devoted to the general methodology of building such systems or present experimental design of systems supporting certain class of decision problems. The third section addresses issues of "Applications of Decision Support Systems and Computer Implementations of Decision Support Systems." Another part of this section has a special character. Beside theoretical and methodological papers, several practical implementations of software for decision support have been presented during the workshop. These software packages varied from very experimental and illustrative implementations of some theoretical concept to well developed and documented systems being currently commercially distributed and used for solving practical problems