Optimization of piezoelectric patches in smart structures using multi-objective genetic algorithms

In this paper multi-objective genetic algorithms have been used to search for the optimal placement of the piezoelectric sensors and actuators bonded on smart beams. A finite element method based on Timoshenko beam theory is used accounting for the piezoelectric layers. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero-one optimization problem with multi-objective functions as performance measures. A cantilever beam example is considered to demonstrate the performance of the selected multi-objective genetic algorithm which is NSGAII. It is shown that the proposed algorithm is effective in developing optimal Pareto front curves for optimal placement and number of actuators and sensors such that the performance on dynamic responses is also satisfied

Multi-guide Particle Swarm Optimisation for Dynamic Multi-objective Optimisation Problems

This study investigates the suitability of, and adapts, the multi-guide particle swarm optimisation (MGPSO) algorithm for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach, originally developed for static multi-objective optimisation problems (SMOPs), where each subswarm optimises one of the objectives. It uses a bounded archive that is based on a crowding distance archive implementation. Compared to static optimization problems, DMOPs pose a challenge for meta-heuristics because there is more than one objective to optimise, and the location of the Pareto-optimal set (POS) and the Pareto-optimal front (POF) can change over time. To efficiently track the changing POF in DMOPs using MGPSO, six archive management update approaches, eight archive balance coefficient initialization strategies, and six quantum particle swarm optimisation (QPSO) variants are proposed. To evaluate the adapted MGPSO for DMOPs, a total of twenty-nine well-known benchmark functions and six performance measures were implemented. Three experiments were run against five different environment types with varying temporal and spatial severities. The best strategies from each experiment were then compared with the other dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that the adapted MGPSO achieves very competitive, and often better, performance compared to existing DMOAs

PI-tuned UPFC damping controllers design for multi-machine power system

This paper presents an adaptive multi-objective algorithm based Unified Power Flow Controller (UPFC) tuned for damping oscillations in two-area multi-machine system formulated as multi- objective optimization problem. The algorithms such as, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified Non-dominated Sorting Genetic Algorithm-II (MNSGA-II) are proposed for tuning the damping controller with speed deviation and control input as conflicting objectives. The proposed algorithm is implemented in the two area multi-machine system using MATLAB Simulink model, and the simulation results were obtained with respect to the characteristics of damping oscillations and the dynamic stability of power systems. The performance measures such as Integral Time Squared Error (ITSE) and Integral Squared Error (ISE) are considered as the objective functions. The results of the two proposed algorithm has been compared and the outcome shows that the MNSGA-II algorithm performs better compared to the NSGA-II algorithm

Optimizing Inventory for Profitability and Order Fulfillment Improvement

Despite the extensive research on inventory management, few studies have investigated the optimization of inventory classification and control policies for maximizing the net present value of profit and order fulfillment performance. This dissertation aims to fill the gaps, and consists of two main essays. Essay One (Chapter 1) presents a new multi-period optimization model to explicitly address nonstationary demand, arbitrary review periods, and SKU-specific lead times, with the objective of maximizing the net present value of profit. A real-world application and computational experiments show that the optimal dynamic inventory classification and control decisions obtained from the model significantly reduce both safety stock and base stock levels compared to a multi-criteria inventory classification scheme and the traditional ABC approach. Essay Two (Chapter 2) examines two order-based fulfillment performance measures: the order fill rate, defined as the percentage of orders that are completely filled from available inventory; and the average customer-order fill rate, defined as the mean percentage of total units in a customer order that can be filled from on-hand inventory. Novel optimization models are developed to maximize the order fulfillment performance. Computational results indicate that a commonly used item-based measure in general does not adequately indicate order-based performance, and the tradeoffs between profit and order-based measures vary with inventory investment. This research contributes to the existing literature by providing new approaches to optimize inventory classification and control policies with various performance criteria. It also provides practitioners with a viable way to manage inventory with nonstationary demand, general review periods and lead times, and further allows companies to quantity the tradeoffs of different performance measures

Power analysis of multi-legged systems

This paper studies periodic gaits of multi-legged robot locomotion systems based on dynamic models. The purpose is to determine the system performance during walking and the best set of locomotion variables that minimizes the optimization indices. For that objective the precribed motion of the robot is completely characterized in terms of several locomotion variables such, gait, duty factor, body height, step length, stroke pitch, foot clearance, leg links length, foot-hip offset, body and legs mass and cycle time. In this perspective, we formulate four performance measures of the walking robot namely, the foot locomobility index, the mean absolute power, the mean power dispersion and the mean power lost in the joint actuators per walking distance. A set of model-based experiments reveals the influence of the locomotion variables in the proposed indices.N/

Seeking multiple solutions:an updated survey on niching methods and their applications

Multi-Modal Optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation run has practical relevance to problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO problems, if equipped with specificallydesigned diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the most recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts at leveraging the capabilities of niching to facilitate various optimization tasks (e.g., multi-objective and dynamic optimization) and machine learning tasks (e.g., clustering, feature selection, and learning ensembles). A list of successful applications of niching methods to real-world problems is presented to demonstrate the capabilities of niching methods in providing solutions that are difficult for other optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving

Handling multiple objectives in optimization of externalities as objectives for dynamic traffic management.

Dynamic traffic management (DTM) is acknowledged in various policy documents as an important instrument to improve network performance. This network performance is not only a matter of accessibility, since the externalities of traffic are becoming more and more important objectives as well. Optimization of network performance using DTM measures is a specific example of a network design problem (NDP) and incorporation of externality objectives results in a multi objective network design problem (MO NDP)). Solving this problem resorts in a Pareto optimal set of solutions. A framework is presented with the non-dominated sorting algorithm (NSGAII), the Streamline dynamic traffic assignment model and several externality models, that is used to solve this MO NDP. With a numerical experiment it is shown that the Pareto optimal set provides important information for the decision making process, which would not have been available if the optimization problem was simplified by incorporation of a compensation principle in advance. However, in the end a solution has to be chosen as the best compromise. Since the Pareto optimal set can be difficult to comprehend, ranking it may be necessary to assist the decision makers. Cost benefit analysis which uses the economic compensation principle is a method that is often used for ranking the alternatives. This research shows, that travel time costs are by far the most dominant objective. Therefore other ranking methods should be considered. Differences between these methods are explained and it is illustrated that the outcomes and therefore the eventual decisions taken can be different

Online optimization of storage ring nonlinear beam dynamics

We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl