Generation maintenance scheduling based on multiple objectives and their relationship analysis

In a market environment of power systems, each producer pursues its maximal profit while the independent system operator is in charge of the system reliability and the minimization of the total generation cost when generating the generation maintenance scheduling （GMS）. Thus, the GMS is inherently a multi-objective optimization problem as its objectives usually conflict with each other. This paper proposes a multi-objective GMS model in a market environment which includes three types of objectives, i.e., each producer＇s profit, the system reliability, and the total generation cost. The GMS model has been solved by the group search optimizer with multiple producers （GSOMP） on two test systems. The simulation results show that the model is well solved by the GSOMP with a set of evenly distributed Pareto-optimal solutions obtained. The simulation results also illustrate that one producer＇s profit conflicts with another one＇s, that the total generation cost does not conflict with the profit of the producer possessing the cheapest units while the total generation cost conflicts with the other producers＇ profits, and that the reliability objective conflicts with the other objectives.     

Handling multiple objectives with particle swarm optimization

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.     

MulRoGA: A Multicast Routing Genetic Algorithm approach considering multiple objectives

We solve a multicast routing problem by means of a genetic algorithm (GA) without using multicast trees. The source-destination routes need to fulfill two conflicting objectives: maximization of the common links and minimization of the route sizes. The proposed GA can be characterized by its representation of network links and routes in a variable size multi-chromosome problem; local viability restrictions in order to generate the initial population and define variation operators; selection operators in order to choose the most promising individuals thus preserving diversity, and the fitness function in order to handle the conflicting multiple objectives. The proposed model is called a Multicast Routing Genetic Algorithm (MulRoGA). The model was tested on the 33-node European GéANT WAN network backbone and three other networks (66-node, 100-node and 200-node) randomly generated using the Waxman model on a network topology generator BRITE. On considering each network, a number of solutions were found for changes in the size and node members of the multicast groups, and the source node. The results of the MulRoGA operation suggest a consistent and robust performance in the various cases including comparisons with the methods of unicast shortest path routing, shortest path tree routing (SPT), and simulated annealing (SA) heuristic.     

Truss optimization considering homologous deformation under multiple loadings

The deformation of a structure shall be called homologous, if a given geometrical relation holds for a given number of structural points before, during, and after the deformation. Some researchers have utilized the idea of structural design with the finite element method. The approaches use the decomposition of the FEM equation or equality equations to obtain homologous deformation. However, weight reduction and response constraints such as stress, displacement or natural frequency cannot be considered by those theories. An optimization method solving the above problems is suggested for obtaining homologous deformation. Homology constraints can be considered under multiple loading conditions as well as a single loading condition. A homology index is defined for multiple loading conditions. Examples are solved to demonstrate the performance of the method.     

Particle swarm optimization based inverter design considering transient performance

Evolutionary computation is an efficient tool for automated design of digital integrated circuits. Demand for electronic circuit automation has increased due to complexity growth in VLSI circuits. Since circuit design deals with highly complicated nonlinear equations, obtaining optimal solution of these equations due to particular constraints in short time and disregardable error is of prime concern. Simpler structure and better result providing in case of parameter growth makes particle swarm optimization (PSO) an ideal candidate for optimal design of circuit topologies. In this work, usage of PSO algorithm in digital electronic circuit design has been investigated. For this purpose, the performance of the algorithm has been tested on the design of an inverter considering transient performance. Performance criteria of inverter constitute the constraints of PSO. Obtained results show that theoretical design of inverter is matched with PSO based design.     

Structural optimization based on topology optimization techniques using frame elements considering cross-sectional properties

This paper discusses a new structural optimization method, based on topology optimization techniques, using frame elements where the cross-sectional properties can be treated as design variables. For each of the frame elements, the rotational angle denoting the principal direction of the second moment of inertia is included as a design variable, and a procedure to obtain the optimal angle is derived from Karush–Kuhn–Tucker (KKT) conditions and a complementary strain energy-based approach. Based on the above, the optimal rotational angle of each frame element is obtained as a function of the balance of the internal moments. The above methodologies are applied to problems of minimizing the mean compliance and maximizing the eigen frequencies. Several examples are provided to show the utility of the presented methodology.     

Simulation based optimization of a train maintenance facility

In this paper, a simulation based optimization method is developed for optimization of scheduling policies. This method uses the technique of coupling industrial simulation software with a multi-objective optimizer based on genetic algorithms. It is used to optimize the performances of a railway maintenance facility by choosing the best scheduling policy. Numerical results show that a significant improvement is achieved with respect to the simulation results of the existing system. The method adapted by our problem can be extended to deal with the selection of scheduling rules in using other types of simulation models.     

Controller design with multiple objectives

In this paper we study multi-objective control problems that give rise to equivalent convex optimization problems. We develop a uniform treatment of such problems by showing their equivalence to linear programming problems with equality constraints and an appropriate positive cone. We present some specialized results on duality theory, and we apply them to the study of three multi-objective control problems: the optimal l₁ control with time-domain constraints on the response to some fixed input, the mixed H₂/l₁ -control problem, and the l₁ control with magnitude constraint on the frequency response. What makes these problems complicated is that they are often equivalent to infinite-dimensional optimization problems. The characterization of the duality relationship between the primal and dual problem allows us to derive several results. These results establish connections with special convex problems (linear programming or linear matrix inequality problems), uncover finite-dimensional structures in the optimal solution, when possible, and provide finite-dimensional approximations to any degree of accuracy when the problem does not appear to have a finite-dimensional structure. To illustrate the theory and highlight its potential, several numerical examples are presented     

Path planning with multiple objectives

Most path planners are designed to generate a single path that is optimal in terms of some criterion such as path length or travel time. However, for realistic terrain navigation we wish to find a path that is reasonable to execute in a given environment. Therefore we must consider several factors, such as safety, time, and energy consumption. In this article the authors investigate how to find a set of paths (as opposed to a single path) so as to permit various choices concerning multiple criteria. They present simulation results to demonstrate the feasibility of the approach and discuss an extension to navigation in time-varying scenes     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.     

Scatter search based interactive multi-criteria optimization of fuzzy objectives for coal production planning

We present an interactive multi-criteria procedure that uses user defined tradeoff-cutting planes to identify promising feasible solution search space. New solutions in the promising feasible solution search space are constructed using combination of scatter and random search. The procedure of identifying tradeoff-cutting planes and scatter search continues for either a predetermined fixed number of iterations or until no solutions in the promising feasible solution search space are found. We formulate a coal production planning problem with fuzzy profit and fuzzy coal quality decision-maker utilities, and apply our procedure for additive and multiplicative decision-maker utilities.     

A surrogate model based nested optimization framework for inverse problem considering interval uncertainty

The inverse problem is a kind of engineering problem that estimates the input through the given output. In this paper, the given output described as interval uncertainty parameter is concerned, and the interval is formed by the interval midpoint and the interval radius. The two-step framework that estimates the midpoint and the radius separately is used. A novel nested optimization framework is proposed to estimate the input interval radius with more inputs than outputs. The nested framework has two loops: (i) the inner loop quantifies the lower and upper bounds of the output with given interval radiuses of inputs from the outer loop by two optimizations, and the results will be feedback to the outer loop as constraint values of outer loop; (ii) the outer loop maximizes the input interval radiuses to reduce the cost while meeting the constraints transformed from the given interval. The nested framework induces a high number of forward model computations in the loops and may lead to an unacceptable computational burden for most engineering applications. Therefore, the surrogate model is suggested. The Radial Basis Functions (RBF) surrogate model is used to relieve the computational burden. The effectiveness and the accuracy of the framework are verified through a mathematical example, a cantilever tube example and an airfoil example.     

Reliability based multidisciplinary design optimization of cooling turbine blade considering uncertainty data statistics

Considering the coupling among aerodynamic, heat transfer and strength, a reliability based multidisciplinary design optimization method for cooling turbine blade is introduced. Multidisciplinary analysis of cooling turbine blade is carried out by sequential conjugated heat transfer analysis and strength analysis with temperature and pressure interpolation. Uncertainty data including the blade wall, rib thickness, elasticity Modulus and rotation speed is collected. Data statistics display the probability models of uncertainty data follow three-parameter Weibull distribution. The thickness of blade wall, thickness and height of ribs are chosen as design variables. Kriging surrogate model is introduced to reduce time-consuming multidisciplinary reliability analysis in RBMDO loop. The reliability based multidisciplinary design optimization of a cooling turbine blade is carried out. Optimization results shows that the RBMDO method proposed in this work improves the performance of cooling turbine blade availably.

     

Analysis of robust performance with multiple objectives

Performance robustness with multiple objectives of linear control systems having structured norm-bounded uncertainty is considered. In order to deal with any two objective functions Ψ₁ and Ψ₂ associated with a single feedback control system, the combined criterion Ψ[Ψ₁ ψ₂]Ψ S 1 is often used. The paper, however, considers Ψ ψ₁ Ψ 1 and Ψ W, 11 1 directly. It is shown that it is possible to assess robust performance of two objectives by a single μ test if repeated nonscalar blocks are adequately introduced in the structure of μ. In other words, a performance robustness problem with multiple objectives is proved to be equivalent to a stability robustness problem with extra repeated uncertainty blocks. This equivalence theorem is applicable to various system and norm setups including sampled-data systems.     

A computer based application of non-linear multiple objective optimization

Multiple Objective Optimization Theory (MOOT) techniques are receiving increasing attention due to their ability to incorporate salient non-commensurate and conflicting objectives of a situation into the choice making process. This effort describes the modeling and optimization of an application which utilizes computer aided MOOT for candidate policy evaluation prior to a production process. The specific application discussed is an airborne tactical missile where the pre-production decision depends on such attributes as reliability, cost, technical performance, and survivability. A modeling and analysis effort produced non-linear performance indicers of the aforementioned attributes, and a set of constraints. This vector optimization problem is solved by implementing a constraint optimization technique on a digital computer. The resulting non-dominated solution set provides the information needed to proceed to a production stage.     

Level set topology optimization considering damage

This paper provides a level set based topology optimization approach to design structures exhibiting resistance to damage. The geometry of the structures is represented by the level set method. The design domains are discretized by the extended finite element method allowing for fixed non conforming meshes. The mechanical model represents quasi-brittle materials. Undamaged material behavior is assumed linear elastic while a loss of stiffness is introduced through a non-local damage model. Small strains are assumed. The sensitivities are evaluated by an analytical derivation of the discretized governing equations of the system and considering the adjoint approach. As the damage process is irreversible, the structural responses are path-dependent and this dependency is accounted for in the sensitivity analysis. The optimization problems are solved by mathematical programming algorithms, in particular using the GCMMA scheme. The proposed approach is illustrated with two dimensional examples that highlight the influence of degradation on the optimized designs.     

基于部落结构的多种学习机制蝙蝠优化算法

蝙蝠算法(bat algorithm,BA)是受自然界中的蝙蝠通过回声定位进行搜寻、捕食行为的启发演变而来的一种新颖的群智能仿生优化算法。为了提高蝙蝠算法的收敛效率,把多种学习机制引入到蝙蝠优化算法中,通过将蝙蝠群体进行部落划分以及各部落间建立相互学习机制,使得内部局部搜索及全局最优信息能够在群体内传递。仿真结果表明,该算法切实提高了收敛效率。     

An intelligent moving object optimization algorithm for design problems with mixed variables, mixed constraints and multiple objectives

This paper presents an optimization algorithm for engineering design problems having a mix of continuous, discrete and integer variables; a mix of linear, non-linear, differentiable, non-differential, equality, inequality and even discontinuous design constraints; and conflicting multiple design objectives. The intelligent movement of objects (vertices and compounds) is simulated in the algorithm based on a Nelder–Mead simplex with added features to handle variable types, bound and design constraints, local optima, search initiation from an infeasible region and numerical instability, which are the common requirements for large-scale, complex optimization problems in various engineering and business disciplines. The algorithm is called an INTElligent Moving Object algorithm and tested for a wide range of benchmark problems. Validation results for several examples, which are manageable within the scope of this paper, are presented herein. Satisfactory results have been obtained for all the test problems, hence, highlighting the benefits of the proposed method.