Decision methods for some optimization problems in recognition theory

By optimizing different models of recognition algorithms, a number of discrete extreme problems appear. The search for the maximum solvable subsystem of the system of linear inequalities is one of these tasks. In certain cases additional requirements on a desired solvable subsystem can be imposed. The solution methods for some types of such tasks are proposed.     

Fair solutions for some multiagent optimization problems

We consider optimization problems in a multiagent setting where a solution is evaluated with a vector. Each coordinate of this vector represents an agent’s utility for the solution. Due to the possible conflicts, it is unlikely that one feasible solution is optimal for all agents. Then, a natural aim is to find solutions that maximize the satisfaction of the least satisfied agent, where the satisfaction of an agent is defined as his relative utility, i.e., the ratio between his utility for the given solution and his maximum possible utility. This criterion captures a classical notion of fairness since it focuses on the agent with lowest relative utility. We study worst-case bounds on this ratio: for which ratio a feasible solution is guaranteed to exist, i.e., to what extend can we find a solution that satisfies all agents? How can we build these solutions in polynomial time? For several optimization problems, we give polynomial-time deterministic algorithms which (almost always) achieve the best possible ratio.     

Decision methods for some discrete extreme problems in recognition theory

By optimizing different models of recognition algorithms, a number of discrete extreme problems appear. The search for the maximum solvable subsystem of the system of linear inequalities is one of these tasks. The solution algorithm for this problem is described. This algorithm is effective for linear systems of small ranks. Also, an approximate method that is effective for systems of large dimensionality is proposed. The text was submitted by the author in English. Natalja N. Katerinochkina. Born 1945. Graduated from the Faculty of Mechanics and Mathematics, Moscow State University, in 1967. Received candidates degree in Physics and Mathematics in 1978. The senior scientific worker at the Dorodnicyn Computing Centre, Russian Academy of Science. Scientific interests: discrete mathematics, mathematical cybernetics, pattern recognition, discrete optimization. Author of 35 publications.     

One approach to data analysis in recognition problems on precedents

One approach to data analysis in recognition problems on precedents is investigated. The search task for logical regularities of the classes is considered. The concept of elementary predicate is introduced. This predicate determines the belonging of the object to any half-space in the space of features. The logical regularities, which are the disjunctive forms from elementary predicates, are examined. The search methods for these logical regularities are proposed. These methods are based on the constructing convex hulls of subsets of the training sample.     

Exact analytical solutions for some popular benchmark problems in topology optimization

The aim of this paper is to provide the exact analytical truss solutions for some benchmark problems, which are often used as test examples in both discretized layout optimization of trusses and variable topology (or generalized) shape optimization of perforated plates under plane stress.     

Exact solutions of some optimization problems of transport logistics

A problem for constructing the shortest cyclic route that ensures homogeneous cargo is delivered from producers to consumers by a transport vehicle of limited capacity is considered. Formalizations in the form of Boolean quadratic programming and linear integer programming problems are given. Comparative analysis of the efficiency of three exact algorithms is performed. The problem of finding the minimal admissible capacity of the transport vehicle is considered as an auxiliary problem. Dependence of the length of the optimal route on the capacity of the transport vehicle is studied experimentally.     

Improved cuckoo search for reliability optimization problems

An efficient approach to solve engineering optimization problems is the cuckoo search algorithm. It is a recently developed meta-heuristic optimization algorithm. Normally, the parameters of the cuckoo search are kept constant. This may result in decreasing the efficiency of the algorithm. To cope with this issue, the cuckoo search parameters should be tuned properly. In this paper, an improved cuckoo search algorithm, enhancing the accuracy and convergence rate of the cuckoo search algorithm, is presented. Then, the performance of the proposed algorithm is tested on some complex engineering optimization problems. They are four well-known reliability optimization problems, a large-scale reliability optimization problem as well as a complex system, which is a 15-unit system reliability optimization problem. Finally, the results are compared with those given by several well-known methods. Simulation results demonstrate the effectiveness of the proposed algorithm.     

A general one-dimensional search algorithm for computing approximate solutions of some non-linear optimal control problems

Barr and Gilbert (1966, 1969 b) have presented computing algorithms for converting a brood class of optimal control problems (including minimum time, and fixed-time minimum fuel, energy and effort problems) to a sequence of optimal regulator problems, using a one dimensional search of the cost variable. These Barr and Gilbert algorithms, which use quadratic programming algorithms by the same authors (1969 a) to solve the resulting optimal regulator problems, are restricted to dynamic equations linear in state by virtue of using the convexity and compactness (Neustadt 1963) and contact function (Gilbert 1966) of the reachable set

This paper extends the above approach to a class of terminal cost optimal control problems similar to those considered by Barr and Gilbert (including quite general control constraints, but only allowing initial and final state constraints), having differential equations non-linear instate and control (where the convexity-compactness results do not hold), by converting each such problem to a sequence of optimal regulator problems, with non-linear differential equations. These, in turn, are solved by one of the author's earlier algorithms (Katz 1974) that makes use of the above convexity, compactness, and contact function results by repeatedly linearizing the regulator problems. The approach of this paper differs from that of Halkin (1964 b), in that Halkin directly linearizes the original problem (e.g. converting a non-linear minimum fuel problem to a linear minimum fuel problem) and then solves the linearized version by a doubly iterative procedure

The computing algorithm presented here is based on the definition of an appropriate approximate solution of the terminal cost problem. A local-minimum convergence proof is given, which is weak in the sense that it assumes convergence of the substep algorithm (Katz 1974) for non-linear optimal regulator problems, whose convergence has not been proved. A subsequent paper (Katz and Wachtor, to appear) shows good convergence of the (overall) terminal cost problem algorithm in examples having singular arcs, with no prior knowledge of the solution or its singular nature, other than an initial upper bound on the cost.     

求解函数优化问题的改进布谷鸟搜索算法

为了提高布谷鸟搜索算法求解函数优化问题的求精能力和收敛速度,提出了一种基于自适应机制的改进算法.自适应机制用于控制缩放因子和发现概率,以提高种群的多样性,避免早熟,从而使更多的个体参与演化,达到提高求精能力和收敛速度的效果.仿真实验结果表明,与标准的布谷鸟搜索算法相比,基于自适应机制缩放因子的改进算法(rCS)和基于自适应机制发现概率的改进算法(paCS)在求精能力和收敛速度上都有明显的提高;同时具有自适应缩放因子和自适应发现概率的改进算法(iCS)比rCS和paCS具有更优的求精能力和收敛速度.     

Modified harmony search optimization for constrained design problems

Constrained optimization is a major real-world problem. Constrained optimization problems consist of an objective function subjected to both linear and nonlinear constraints. Here a constraint handling procedure based on the fitness priority-based ranking method (FPBRM) is proposed. It is embedded into a harmony search (HS) algorithm that allows it to satisfy constraints. The HS algorithm is conceptualized using the musical process of searching for a perfect state of harmony. Here, the original heuristic HS was improved by combining both improved and global-best methods along with the FPBRM. The resulting modified harmony search (MHS) was then compared with the original HS technique and other optimization methods for several test problems.     

Methods of searching for guaranteeing and optimistic solutions to integer optimization problems under uncertainty

The paper studies complex integer optimization problems with inexact coefficients of the linear objective function and convex quadratic constraint functions. Exact and approximate decomposition methods are developed and proved to search for guaranteeing and optimistic solutions to such problems. The methods are based on approximation of initial problems by problems of a simpler structure. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 103–114, January–February 2007.     

折半查找算法在最优化问题中的应用

折半查找是一种常见的静态查找方法,在特定的、有序的查找区间内,通过折半方式不断地缩小查找区间,将区间中间位置的元素与给定元素加以比较,最终确定查找结果.在此传统折半查找基础上,总结了一种抽象化的改进方法,并将此改进后的折半查找算法应用于最优化问题的求解.     

Globally optimal benchmark solutions to some small-scale discretized continuum topology optimization problems

In this note, globally optimal solutions to three sets of small-scale discretized continuum topology optimization problems are presented. All the problems were discretized by the use of nine-node isoparametric finite elements. The idea is that these solutions can be used as benchmark problems when testing new algorithms for finding pure 0–1 solutions to topology optimization problems defined on discretized ground structures.     

A differential-based harmony search algorithm for the optimization of continuous problems

The performance of the Harmony Search (HS) algorithm is highly dependent on the parameter settings and the initialization of the Harmony Memory (HM). To address these issues, this paper presents a new variant of the HS algorithm, which is called the DH/best algorithm, for the optimization of globally continuous problems. The proposed DH/best algorithm introduces a new improvisation method that differs from the conventional HS in two respects. First, the random initialization of the HM is replaced with a new method that effectively initializes the harmonies and reduces randomness. Second, the conventional pitch adjustment method is replaced by a new pitch adjustment method that is inspired by a Differential Evolution (DE) mutation strategy known as DE/best/1. Two sets of experiments are performed to evaluate the proposed algorithm. In the first experiment, the DH/best algorithm is compared with other variants of HS based on 12 optimization functions. In the second experiment, the complete CEC2014 problem set is used to compare the performance of the DH/best algorithm with six well-known optimization algorithms from different families. The experimental results demonstrate the superiority of the proposed algorithm in convergence, precision, and robustness.     

Exact analytical solutions for some popular benchmark problems in topology optimization III: L-shaped domains revisited

The paper provides the corrections and extensions of the paper of the same title, published in vol. 35, 2008, pp. 165–174 of the current journal. The analytical solutions of Michell trusses constructed within the L-shaped domains are confirmed by the ground structure method of the truss optimization, thus giving the upper bounds of the exact solutions.     

A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

During the past decade, considerable research has been conducted on constrained optimization problems (COPs) which are frequently encountered in practical engineering applications. By introducing resource limitations as constraints, the optimal solutions in COPs are generally located on boundaries of feasible design space, which leads to search difficulties when applying conventional optimization algorithms, especially for complex constraint problems. Even though penalty function method has been frequently used for handling the constraints, the adjustment of control parameters is often complicated and involves a trial-and-error approach. To overcome these difficulties, a modified particle swarm optimization (PSO) algorithm named parallel boundary search particle swarm optimization (PBSPSO) algorithm is proposed in this paper. Modified constrained PSO algorithm is adopted to conduct global search in one branch while Subset Constrained Boundary Narrower (SCBN) function and sequential quadratic programming (SQP) are applied to perform local boundary search in another branch. A cooperative mechanism of the two branches has been built in which locations of the particles near boundaries of constraints are selected as initial positions of local boundary search and the solutions of local boundary search will lead the global search direction to boundaries of active constraints. The cooperation behavior of the two branches effectively reinforces the optimization capability of the PSO algorithm. The optimization performance of PBSPSO algorithm is illustrated through 13 CEC06 test functions and 5 common engineering problems. The results are compared with other state-of-the-art algorithms and it is shown that the proposed algorithm possesses a competitive global search capability and is effective for constrained optimization problems in engineering applications.     

Exact analytical solutions for some popular benchmark problems in topology optimization II: three-sided polygonal supports

In an earlier paper (Rozvany, Struct Optim 15:42–48, 1998), the second author summarized known analytical solutions for some popular benchmark problems in topology optimization. In this, and in some subsequent papers, further exact optimal topologies are derived for least-weight, stress-controlled trusses, with load and support conditions that are frequently used in benchmark examples for numerical methods in topology optimization.     

Soliton solutions of some nonlinear evolution problems by GKM

Neural Computing and Applications - In this paper, we establish exact solutions of coupled Higgs equation and Nizhnik-Novikov-Veselov (NNV) system. We apply generalized Kudryashov method (GKM) to...