Multiple Objective Functions for Falsification of Cyber-Physical Systems

Cyber-physical systems are typically safety-critical, thus it is crucial to guarantee that they conform to given specifications, that are the properties that the system must fulfill. Optimization-based falsification is a model-based testing method to find counterexamples of the specifications. The main idea is to measure how far away a specification is from being broken, and to use an optimization procedure to guide the testing towards falsification. The efficiency of the falsification is affected by the objective function used to evaluate the test results; different objective functions are differently efficient for different types of problems. However, the efficiency of various objective functions is not easily determined beforehand. This paper evaluates the efficiency of using multiple objective functions in the falsification process. The hypothesis is that this will, in general, be more efficient, meaning that it falsifies a system in fewer iterations, than just applying a single objective function to a specific problem. Two objective functions are evaluated, Max, Additive, on a set of benchmark problems. The evaluation shows that using multiple objective functions can reduce the number of iterations necessary to falsify a property

Energy Efficiency Optimization of Intelligent Reflective Surface-assisted Terahertz-RSMA System

This paper examines the energy efficiency optimization problem of intelligent reflective surface (IRS)-assisted multi-user rate division multiple access (RSMA) downlink systems under terahertz propagation. The objective function for energy efficiency is optimized using the salp swarm algorithm (SSA) and compared with the successive convex approximation (SCA) technique. SCA technique requires multiple iterations to solve non-convex resource allocation problems, whereas SSA can consume less time to improve energy efficiency effectively. The simulation results show that SSA is better than SCA in improving system energy efficiency, and the time required is significantly reduced, thus optimizing the system's overall performance

On optimal designs for clinical trials: An updated review

Optimization of clinical trial designs can help investigators achieve higher qualityresults for the given resource constraints. The present paper gives an overviewof optimal designs for various important problems that arise in different stages ofclinical drug development, including phase I dose–toxicity studies; phase I/II studiesthat consider early efficacy and toxicity outcomes simultaneously; phase IIdose–response studies driven by multiple comparisons (MCP), modeling techniques(Mod), or their combination (MCP–Mod); phase III randomized controlled multiarmmulti-objective clinical trials to test difference among several treatment groups;and population pharmacokinetics–pharmacodynamics experiments. We find thatmodern literature is very rich with optimal design methodologies that can be utilizedby clinical researchers to improve efficiency of drug development

On The Karush – Kuhn – Tucker Reformulation of Bi – Level Geometric Programming Problem with an Interval Coefficients as Multiple Parameters

This paper presents a new approach to solve a special class of bi – level nonlinear programming (NLP) problems with an interval coefficients as multiple parameters. Geometric programming (GP) is a powerful technique developed for solving nonlinear programming (NLP) problems and it is useful in the study of a variety of optimization problems. Many applications of GP in various fields of science and engineering are used to solve certain complex decision making problems. In this paper a new mathematical formulations for a new class of nonlinear optimization models called bi – level geometric programming (BLGP) problem is presented. This problems are not necessarily convex and thus not solvable by standard nonlinear programming techniques. This paper proposed a method to solve BLGP problem where coefficient of objective function as well as coeffiaent of constraints are multiple parameters. Especially the multiple parameters are considered in an interval which are the Arithmetic mean (A.M), Geometric mean (G.M) and Harmonic mean (H. M) of the end points of the interval. In this paper, the values of objective function in interval range of parameters for A. M., G. M. and H. M. are preserved the same relationship. Also, BLGP problem can be converted to a single objective by using the classical karush – kuhn – Tucker (KKT) reformulation and the ability of calculating the bounds of objective value in KKT is basically presented in this paper that may help researchers in constructing more realistic model in optimization field.  Finally, numerical example is given to illustrate the efficiency of the method

Solving Incremental Optimization Problems via Cooperative Coevolution

Engineering designs can involve multiple stages, where at each stage, the design models are incrementally modified and optimized. In contrast to traditional dynamic optimization problems where the changes are caused by some objective factors, the changes in such incremental optimization problems are usually caused by the modifications made by the decision makers during the design process. While existing work in the literature is mainly focused on traditional dynamic optimization, little research has been dedicated to solving such incremental optimization problems. In this work, we study how to adopt cooperative coevolution to efficiently solve a specific type of incremental optimization problems, namely, those with increasing decision variables. First, we present a benchmark function generator on the basis of some basic formulations of incremental optimization problems with increasing decision variables and exploitable modular structure. Then, we propose a contribution based cooperative coevolutionary framework coupled with an incremental grouping method for dealing with them. On one hand, the benchmark function generator is capable of generating various benchmark functions with various characteristics. On the other hand, the proposed framework is promising in solving such problems in terms of both optimization accuracy and computational efficiency. In addition, the proposed method is further assessed using a real-world application, i.e., the design optimization of a stepped cantilever beam