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Subjects / keywords: Computer science; Optimization algorithm; Algorithm; Mathematical optimization; Mathematics

Due to the safety risks and training sample inefficiency, it is often preferred to develop controllers in simulation. However, minor differences between the simulation and the real world can cause a significant sim-to-re...

Having developed multiobjective optimization algorithms using evolutionary optimization methods and demonstrated their niche on various practical problems involving mostly two and three objectives, there is now a growing...

Subjects / keywords: Local optimum; Algorithm; Mathematical optimization; Trigonometric functions; Optimization problem; Convergence (economics); Mathematics; Sine; Set (abstract data type); Global optimization; Computer science; Economics

Subjects / keywords: Whale; Optimization algorithm; Meta heuristic; Computer science; Mathematical optimization; Heuristic; Algorithm; Artificial intelligence; Mathematics; Fishery; Biology

In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is know...

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be...

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has not yet been widely used in multiobjective evolutionary optimization. This paper proposes a multiobjective evolutionary algori...

Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been develope...

A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algori...

SeDuMi is an add-on for MATLAB, that lets you solve optimization problems with linear, quadratic and semidefiniteness constraints. It is possible to have complex valued data and variables in SeDuMi. Moreover, large scale...

The MATLAB toolbox YALMIP is introduced. It is described how YALMIP can be used to model and solve optimization problems typically occurring in systems and control theory. In this paper, free MATLAB toolbox YALMIP, devel...

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is th...

A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the...

Subjects / keywords: Broyden–Fletcher–Goldfarb–Shanno algorithm; Quasi-Newton method; Convergence (economics); Mathematical optimization; Mathematics; Scale (ratio); Computer science; Applied mathematics; Newton's method; Nonlinear sys...

Subjects / keywords: Particle swarm optimization; Swarming (honey bee); Computer science; Multi-swarm optimization; Metaheuristic; Swarm behaviour; Snapshot (computer storage); Mathematical optimization; Algorithm; Artificial intelligence; M...

A number of extensions to the multisolution approach to the crystallographic phase problem are discussed in which the negative quartet relations play an important role. A phase annealing method, related to the simulated ...

Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedu...

Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manip...

Little has been done in the study of these intriguing questions, and I do not wish to give the impression that any extensive set of ideas exists that could be called a "theory." What is quite surprising, as far as the hi...

Abstract A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n >...

This is the second half of a two part series devoted to the tabu search metastrategy for optimization problems. Part I introduced the fundamental ideas of tabu search as an approach for guiding other heuristics to overco...

Subjects / keywords: Boosting (machine learning); Multiplicative function; Bounded function; Computer science; Generalization; Mathematical optimization; Decision rule; Artificial intelligence; Mathematics; Machine learning; Algorithm; Mathe...

A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n + 1) vertices of a general simplex, followed by the replacement of the vertex with the...