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A Multi-Objective Memetic Optimization Method for Power Network Cascading Failures Protection
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Cutting tool wear degrades the product quality in manufacturing processes. Hence, real-time online estimation of tool wear is important for suggesting a tool replacement before the wear limit is reached, in order to protect the workpiece and the CNC machine from damage and breakdown. In this study, using both statistical features and wavelet features extracted from sensor signals, an adaptive evolutionary extreme learning machine (ELM) learning paradigm is developed for tool wear estimation in high-speed milling process. In the proposed method, a discrete differential evolution (DE) algorithm is used to select input features for the ELM, and a continuous DE algorithm is used for parameter optimisation of the mixed kernel function for the ELM. The experimental results indicate that the proposed adaptive evolutionary ELM-based tool wear estimation model can effectively estimate the tool wear in high-speed milling process. Empirical comparisons show that the proposed model performs better than existing approaches in estimating the tool wear.
A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation.
Infrastructure interdependency stems from the functional and logical relations among individual components in different distributed systems. To characterize the extent to which a contingency affecting an infrastructure is going to weaken, and possibly disrupt, the safe operation of an interconnected system, it is necessary to model the relations established through the connections linking the multiple components of the involved infrastructures. In this work, the modeling of interdependencies among network systems and of their effects on failure propagation is carried out within the simulation framework of a failure cascade process. The sensitivity of the critical loading value (the lower bound of the cascading failure region) and of the average cascade size with respect to the coupling parameters defining the interdependency strength is investigated as a means to arrive at the definition and prescription of cascade-safe operating margins.
This paper addresses the optimization of protection strategies in critical infrastructures within a complex network systems perspective. The focus is on cascading failures triggered by the intentional removal of a single network component. Three different protection strategies are proposed that minimize the consequences of cascading failures on the entire system, on predetermined areas or on both scales of protective intervention in a multi-objective optimization framework. We optimize the three protection strategies by devising a modified binary differential evolution scheme that overcomes the combinatorial complexity of this optimization problem. We exemplify our methodology with reference to the topology of an electricity infrastructure, i.e. the 380 kV Italian power transmission network. We only focus on the structure of this network as a test case for the suggested protection strategies, with no further reference on its physical and electrical properties.
Differential Evolution (DE) is a simple yet efficient global optimization algorithm. However, the standard DE and most of its variants operate in the continuous space, which cannot solve the binary-coded optimization problems directly. To tackle this problem, this paper proposes a novel modified binary differential evolution algorithm (NMBDE) inspired by the concept of Estimation of Distribution Algorithm and DE. A novel probability estimation operator is developed for NMBDE, which can efficiently maintain diversity of population and achieve a better tradeoff between the exploration and exploitation capabilities by cooperating with the selection operator. Furthermore, the parameter study of NMBDE is run and the analysis is performed to improve the global search ability and scalability of algorithm. The effectiveness and efficiency of NMBDE was verified in applications to the numerical optimization and multidimensional knapsack problems. The experimental results demonstrate that NMBDE has the better global search ability and outperforms the discrete binary DE, the modified binary DE, the discrete binary Particle Swarm Optimization and the binary Ant System in terms of accuracy and convergence speed.
Harmony search method based optimum design algorithm is presented for the steel sway frames. The harmony search method is a numerical optimization technique developed recently that imitates the musical performance process which takes place when a musician searches for a better state of harmony. Jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The optimum design algorithm developed imposes the behavioral and performance constraints in accordance with BS5950. The member grouping is allowed so that the same section can be adopted for each group. The combined strength constraints considered for a beam–column take into account the lateral torsional buckling of the member. The algorithm presented selects the appropriate sections for beams and columns of the steel frame from the list of 64 Universal Beam sections and 32 Universal Column sections of the British Code. This selection is carried out so that the design limitations are satisfied and the weight of steel frame is the minimum. The number of design examples considered to demonstrate the efficiency of the algorithm is presented.
In this chapter, we discuss various issues related to the implementation of multiobjective memetic algorithms (MOMAs) for
combinatorial optimization problems. First we explain an outline of our MOMA, which is a hybrid algorithm of NSGA-II and local
search. Our MOMA is a general framework where we can implement a number of variants. For example, we can use Pareto dominance
as well as scalarizing functions such as a weighted sum in local search. Through computational experiments on multiobjective
knapsack problems, we examine various issues in the implementation of our MOMA. More specifically, we examine the following
issues: the frequency of local search, the choice of initial solutions for local search, the specification of an acceptance
rule of local moves, the specification of a termination condition of local search, and the handling of infeasible solutions.
We also examine the dynamic control of the balance between genetic operations (i.e., global search) and local search during
the execution of our MOMA. Experimental results show that the hybridization with local search does not necessarily improve
the performance of NSGA-II whereas local search with problem-specific knowledge leads to drastic performance improvement.
Experimental results also show the importance of the balance between global search and local search. Finally we suggest some
future research issues in the implementation of MOMAs.
This chapter sets forth a new approach for transmission network expansion planning that accounts for increasingly plausible
deliberate outages. Malicious attacks expose the network planner, a centralized entity responsible for expansion decisions
of the entire transmission network, to a new challenge: how to expand and reinforce the transmission network so that the vulnerability
against intentional attacks is mitigated while meeting budgetary limits. Two vulnerability-constrained transmission expansion
models are presented in this chapter. The first model allows the network planner to analyze the tradeoff between economic-
and vulnerability-related issues and its impact on the expansion plans. The uncertainty associated with intentional outages
is modeled through scenarios. Vulnerability is measured in terms of the system load shed. In the second model, the risk associated
with the nonrandom uncertainty of deliberate outages is incorporated through the minimax weighted regret criterion. The proposed
models are formulated as mixed-integer linear programs for which efficient solvers are available. Illustrative examples show
the performance of both models.
KeywordsDeliberate outages-Minimax weighted regret-Mixed-integer linear programming-Risk-Scenario-Transmission network expansion planning-Vulnerability