Infeasible Ones Research Articles

An authentication scheme for color images with grayscale invariance and recoverability using image demosaicing

This paper proposes a novel recoverable authentication scheme for color images that ensures the consistency of grayscale values after embedment. In the proposed scheme, the color image is sub-sampled based on the Bayer pattern, and the most significant bits (MSBs) of the sub-sampled image are used as the recovery codes. Image blocks are divided into two categories, feasible and infeasible. Feasible blocks are utilized for embedding both authentication and recovery codes, whereas infeasible ones are only used for embedding authentication codes. In case of tampering, the authentication codes are employed to detect the tampered regions, and the undamaged recovery codes can be utilized to reconstruct the sub-sampled image. The demosaicing algorithm is applied to the sub-sampled image to obtain the demosaicked image, which is then used to recover the tampered regions. Experiments demonstrate that the proposed scheme yields an acceptable image quality even when the tampering rate is as high as 40 %.

Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning

Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic accessibility as a pre-retrosynthesis heuristic. A wide range of machine learning scoring approaches is available, however, their applicability and correctness were studied to a limited extent. Moreover, there is a lack of critical assessment of synthetic accessibility scores with common test conditions.In the present work, we assess if synthetic accessibility scores can reliably predict the outcomes of retrosynthesis planning. Using a specially prepared compounds database, we examine the outcomes of the retrosynthetic tool AiZynthFinder. We test whether synthetic accessibility scores: SAscore, SYBA, SCScore, and RAscore accurately predict the results of retrosynthesis planning. Furthermore, we investigate if synthetic accessibility scores can speed up retrosynthesis planning by better prioritizing explored partial synthetic routes and thus reducing the size of the search space. For that purpose, we analyze the AiZynthFinder partial solutions search trees, their structure, and complexity parameters, such as the number of nodes, or treewidth.We confirm that synthetic accessibility scores in most cases well discriminate feasible molecules from infeasible ones and can be potential boosters of retrosynthesis planning tools. Moreover, we show the current challenges of designing computer-assisted synthesis planning tools. We conclude that hybrid machine learning and human intuition-based synthetic accessibility scores can efficiently boost the effectiveness of computer-assisted retrosynthesis planning, however, they need to be carefully crafted for retrosynthesis planning algorithms.The source code of this work is publicly available at https://github.com/grzsko/ASAP.

Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore, a PSO is adapted for constrained optimization, yielding two constrained variants, denoted SF-PSO and VCH-PSO. Both SF-PSO and VCH-PSO are evaluated with respect to five engineering problems: the Himmelblau’s nonlinear optimization, the welded beam design, the spring design, the pressure vessel design, and the three-bar truss design. The simulation results show that both algorithms are consistent in terms of their solutions to these problems, including their different available versions. Comparison of the SF-PSO and the VCH-PSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used. We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.

Evaluasi Kelayakan Berdasarkan Aspek Geologi Lingkungan Untuk Penentuan TPA Kota Palangka Raya

Regional feasibility analysis of landfill locations based on the environmental geology point is the initial selection in the planning of landfill sites. This research aims to assess the regional feasibility zones for landfill site in Palangka Raya city based on the environmental aspects. Observational examination and spatial analysis using geographic information system (GIS) were applied to ascertain the determination and limitation factors. The result shows that Palangka Raya could be categorized into 3 (three) zones in terms of its feasibility for the landfill area. Unsuitable zone covering an area of 165,936.69 hectares or 58.12% of total area, followed by a moderately suitable zone of 782.25 hectares (32.85%), and least suitable zone of 25,788.88 hectares (9.03%). The infeasible ones are located in districts of Sebangau and Pahandut, and some parts of Jekan Raya.

An estimation of distribution algorithm for scheduling problem of flexible manufacturing systems using Petri nets

• This is the first report on applying estimation of distribution algorithm (EDA) to the studied problem. • A kind of PN-based deadlock controllers for FMSs is imbedded to exclude infeasible individuals. • An effective voting procedure is adopted to construct the probabilistic model of EDA. • The longest common subsequence is also embedded in the model for mining excellent genes. • A new modified variable neighborhood search is developed as an efficiency enhancement of EDA. Based on the place-timed Petri net models of flexible manufacturing systems (FMSs), this paper proposes a novel effective estimation of distribution algorithm (EDA) for solving the scheduling problem of FMSs. A candidate solution is represented as an individual with two sections: the first contains the route information while the second is a permutation with repetition for parts. The feasibility of individuals is checked and guaranteed by a highly permissiveness deadlock controller. A feasible individual is interpreted into a deadlock-free schedule while the infeasible ones are amended. The probabilistic model in EDA is constructed via a voting procedure. An offspring individual is then produced based on the model from a seed individual, and the set of seed individuals is extracted by a roulette method from the current population. The longest common subsequence is also embedded into the probabilistic model for mining good genes. A modified variable neighborhood search is applied on offspring individuals to obtain better solutions in their neighbors and hence to improve EDA’s performance. Computational results show that our proposed algorithm outperforms all the existing ones on benchmark examples for the studied problem. It is of important practice significance for the manufacturing of time-critical and multi-type products.

A modified nature inspired meta-heuristic whale optimization algorithm for solving 0–1 knapsack problem

The Whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social character of the humpback whales. WOA has an efficient performance in solving the continuous problems and engineering optimization problems. This paper presents an improved whale optimization algorithm (IWOA) for solving both single and multidimensional 0–1 knapsack problems with different scales. A penalty function is added to the evaluation function so that the fitness of the feasible solutions can outperform the fitness of the infeasible ones. The sigmoid function can take the real-valued solutions as input and produces the binary solutions as output. A two-stage repair operator is employed for handling the infeasible solutions. IWOA can give a better tradeoff between the diversification and the intensification through using two strategies: Local Search Strategy (LSS) and the Levy flight walks. In addition to the bitwise operation which increases the IWOA efficiency. The proposed IWOA is compared with other state-of-art algorithms to validate the effectiveness of the proposed algorithm in solving 0–1 knapsack problems. Further, the experimental results and extensive numerical illustrations have been indicated that IWOA is efficient, effective, and robust for solving the hard 0–1 knapsack problems than the other existing approaches in the available literature. The source code of the IWOA algorithm will be available after the paper accepted as a toolbox in MATLAB library.

Online Regenerator Placement

Connections between nodes in optical networks are realized by lightpaths. Due to the decay of the signal, a regenerator has to be placed on every lightpath after at most d hops, for some given positive integer d. A regenerator can serve only one lightpath. The placement of regenerators has become an active area of research during recent years, and various optimization problems have been studied. The first such problem is the Regeneration Location Problem (Rlp), where the goal is to place the regenerators so as to minimize the total number of nodes containing them. We consider two extreme cases of online Rlp regarding the value of d and the number k of regenerators that can be used in any single node. (1) d is arbitrary and k unbounded. In this case a feasible solution always exists. We show an O(log|X|⋅ logd)-competitive randomized algorithm for any network topology, where X is the set of paths of length d. The algorithm can be made deterministic in some cases. We show a deterministic lower bound of ${\Omega } \left (\frac {\log (|{E}|/d) \cdot \log d}{\log (\log (|{E}|/d) \cdot \log d)}\right )$ , where E is the edge set. (2) d = 2 and k = 1. In this case there is not necessarily a solution for a given input. We distinguish between feasible inputs (for which there is a solution) and infeasible ones. In the latter case, the objective is to satisfy the maximum number of lightpaths. For a path topology we show a lower bound of $\sqrt {l}/2$ for the competitive ratio (where l is the number of internal nodes of the longest lightpath) on infeasible inputs, and a tight bound of 3 for the competitive ratio on feasible inputs.

An effective computation strategy for assessing operational flexibility of high-dimensional systems with complicated feasible regions

The volumetric flexibility index(FIv) of a chemical system can be viewed geometrically as the ratio between the hypervolume of feasible region and that of a hypercube bounded by the expected upper and lower limits of uncertain process parameters. Although several methods have already been developed to compute FIv, none of them are effective for solving the high-dimensional problems defined in nonconvex, non-simply connected or disconnected regions. While the available shortcut approaches are not accurate enough, successful tuning of the algorithmic parameters is mandatory for producing credible estimates with the more elaborate existing strategies.The above practical issues in volume estimation are thoroughly addressed in the present research. The most critical step in the proposed procedure is to characterize the feasible region accurately. To this end, the domain boundaries in parameter space are first identified with the feasible proximity points obtained by following a random line search algorithm. The Delaunay triangulation technique is then implemented to generate simplexes on the basis of such near-boundary points. By checking the centroids of these simplexes, the infeasible ones may be identified and eliminated. Finally, the hypervolumes of all feasible simplexes are summed to determine the volumetric flexibility index.Extensive case studies with 2–7 uncertain parameters have been carried out to show the superior capabilities of the proposed computation strategies.

A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling

The short-term optimal hydrothermal scheduling (STOHS) plays one of the most important roles in power systems operation. The STOHS problem involves the solution of difficult constrained optimization problems that require good computational techniques. This paper proposes a modified chaotic differential evolution (MCDE) approach for the solution of this difficult optimization problem. A repair strategy and a novel selection operation are simultaneously introduced into the MCDE approach for handling constraints of the problem. The repair strategy preserves the feasibility of solutions generated and avoids the use of penalty factors as much as possible. The introduced selection operation makes a not clearly distinction between feasible solutions and infeasible ones at early stage of the algorithm and makes a clearly distinction at the later stage. Additionally, an adaptive regeneration operation is proposed to enhance population diversity and to avoid local optimums. Moreover, a chaotic local search technique is introduced also to accelerate the searching process of the algorithm. The proposed MCDE approach is applied to three well-known hydrothermal test systems in order to verify its feasibility and efficiency. The obtained results are compared with those obtained by other population-based heuristic approaches reported in literature. It is observed from the comparisons that the proposed MCDE approach performs effectively and can yield competitive solutions.

Component-Based Ranking Strategy for Evolutionary Optimization with Sparse Constraints

Most constraint-handling methods in constrained evolutionary optimization usually take advantage of only the valuable information of feasible solutions, while they don’t exploit adequately the information from infeasible ones. In this paper, a concept of “feasible component” is introduced to recognize the characteristics of diverse information extracted from infeasible solutions. Then a component-based ranking strategy is proposed for evolutionary optimization with sparse constraints by integrating feasible components and the idea of stochastic ranking. Experimental results on several problems with sparse constraints show that the component-based ranking strategy performs better than the stochastic ranking.

Efficient geo-graph contiguity and hole algorithms for geographic zoning and dynamic plane graph partitioning

Graph partitioning is an intractable problem that arises in many practical applications. Heuristics such as local search generate good (though suboptimal) solutions in limited time. Such heuristics must be able to explore the solution space quickly and, when the solution space is constrained, differentiate feasible solutions from infeasible ones. Geographic zoning problems allocate some resource (e.g., political representation, school enrollment, police patrols) to contiguous zones modeled by partitions of an embedded planar graph. Each vertex corresponds to an area of the plane (e.g., census block, town, county), and local search moves one area from its current zone to a different zone in each iteration. Enforcing contiguity constraints may require significant computation when the graph is large. While existing algorithms require linear or polylogarithmic time (in the number of vertices) to assess contiguity in each local search iteration, the geo-graph paradigm shows how contiguity can be verified by examining only the set of vertices that border the transferred area (i.e., those areas whose boundaries share at least a single point with the boundary of the transferred area). This paper develops efficient algorithms that examine these vertices more quickly than traditional search-based methods, allowing practitioners to more fully consider their zoning options when creating zones with local search.

CHOMP: Covariant Hamiltonian optimization for motion planning

In this paper, we present CHOMP (covariant Hamiltonian optimization for motion planning), a method for trajectory optimization invariant to reparametrization. CHOMP uses functional gradient techniques to iteratively improve the quality of an initial trajectory, optimizing a functional that trades off between a smoothness and an obstacle avoidance component. CHOMP can be used to locally optimize feasible trajectories, as well as to solve motion planning queries, converging to low-cost trajectories even when initialized with infeasible ones. It uses Hamiltonian Monte Carlo to alleviate the problem of convergence to high-cost local minima (and for probabilistic completeness), and is capable of respecting hard constraints along the trajectory. We present extensive experiments with CHOMP on manipulation and locomotion tasks, using seven-degree-of-freedom manipulators and a rough-terrain quadruped robot.

An improved electromagnetism-like mechanism algorithm for constrained optimization

Many problems in scientific research and engineering applications can be decomposed into the constrained optimization problems. Most of them are the nonlinear programming problems which are very hard to be solved by the traditional methods. In this paper, an electromagnetism-like mechanism (EM) algorithm, which is a meta-heuristic algorithm, has been improved for these problems. Firstly, some modifications are made for improving the performance of EM algorithm. The process of calculating the total force is simplified and an improved total force formula is adopted to accelerate the searching for optimal solution. In order to improve the accuracy of EM algorithm, a parameter called as move probability is introduced into the move formula where an elitist strategy is also adopted. And then, to handle the constraints, the feasibility and dominance rules are introduced and the corresponding charge formula is used for biasing feasible solutions over infeasible ones. Finally, 13 classical functions, three engineering design problems and 22 benchmark functions in CEC’06 are tested to illustrate the performance of proposed algorithm. Numerical results show that, compared with other versions of EM algorithm and other state-of-art algorithms, the improved EM algorithm has the advantage of higher accuracy and efficiency for constrained optimization problems.

A Modified Dna Computing Approach To Tackle The Exponential Solution Space Of The Graph Coloring Problem

Although it has been evidenced that DNA computing is able to solve the graph coloring problem in a polynomial time complexity, but the exponential solution space is still a restrictive factor in applying this technique for solving really large problems. In this paper a modified DNA computing approach based on Adleman-Lipton model is proposed which tackles the mentioned restriction by coloring the vertices one by one. In each step, it expands the DNA strands encoding promising solutions and discards those which encode infeasible ones. A sample graph is colored by simulating the proposed approach and shows a notable reduction in the number of DNA strands used.

Enhancing structural software coverage by incrementally computing branch executability

Structural code coverage criteria have been studied since the early seventies, and now they are well supported by commercial and open-source tools and are commonly embedded in several advanced industrial processes. Most industrial applications still refer to simple criteria, like statement and branch coverage, and consider complex criteria, like modified condition decision coverage, only rarely and often driven by the requirements of certification agencies. The industrial value of structural criteria is limited by the difficulty of achieving high coverage, due to both the complexity of deriving test cases that execute specific uncovered elements and the presence of many infeasible elements in the code. In this paper, we propose a technique that both generates test cases that execute yet uncovered branches and identifies infeasible branches that can be eliminated from the computation of the branch coverage. In this way, we can increase branch coverage to closely approximate full coverage, thus improving its industrial value. The algorithm combines symbolic analysis, abstraction refinement, and a novel technique named coarsening, to execute unexplored branches, identify infeasible ones, and mitigate the state space explosion problem. In the paper, we present the technique and illustrate its effectiveness through a set of experimental results obtained with a prototype implementation.

Design optimization using Subset Simulation algorithm

This paper presents a global optimization algorithm based on Subset Simulation for deterministic optimal design under general multiple constraints. The proposed algorithm is population-based realized with Markov Chain Monte Carlo and a simple evolutionary strategy. Problem-specific constraints are handled by a feasibility-based fitness function that reflects their degree of violation. Based on the constraint fitness function, a double-criterion sorting algorithm is used to guarantee that the feasible solutions are given higher priority over the infeasible ones before their objective function values are ranked. The efficiency and robustness of the proposed algorithm are illustrated using three benchmark optimization design problems. Comparison is made with other well-known stochastic optimization algorithms, such as genetic algorithm, particle swarm optimization and harmony search.

A Control-Oriented Coverage Metric and its Evaluation for Hardware Designs

Problem statement: Dynamic verification, the use of simulation to determine design correctness, is widely used due to its tractability for large hardware designs. A serious limitation of dynamic techniques is the difficulty in determining whether or not a test sequence is sufficient to detect all likely design errors. Coverage metrics are used to address this problem by providing a set of goals to be achieved during the simulation process; if all coverage goals are satisfied then the test sequence is assumed to be complete. Coverage metrics hence evaluate the ability of a test sequence to detect design errors and are essential to the verification process. A key source of difficulty in determining error detection is that the control-flow path traversed in the presence of an error cannot be determined. This problem becomes particularly difficult in case of typical industrial designs involving interaction of control flow paths between concurrent processes. Error detection can only be accurately determined by exploring the set of all control-flow paths, which may be traversed as a result of an error. Also, there is no technique to identify a correlation between coverage metrics and hardware design quality. Approach: We present a coverage metric that determined the propagation of error effects along all possible erroneous control-flow paths across processes. The complexity of exploring multiple control-flow paths was greatly alleviated by heuristically pruning infeasible control-flow paths using the algorithm that we present. We also presented a technique to evaluate coverage metric by examining its ability to ensure the detection of real design errors. We injected errors in the design to correlate their detection with the coverage computed by our metric. Results: Our coverage metric although analyzed all control-flow paths it pruned the infeasible ones and eliminated them from coverage consideration, hence reducing the complexity of generating tests meant to execute them. The metric also correlated better with detection of design errors than some well-studied metrics do. Conclusion: The proposed coverage metric provided high accuracy in measurement of coverage in designs that contain complex control-flow with concurrent processes. It is superior at detecting design error when compared with the metrics it was compared with.

Verifying Complex Interaction between Hardware Processes

Problem statement: Verification of correct functionality of semiconductor devices has been a challenging problem. Given the device fabrication cost, it is critical to verify the expected functionality using simulations of executable device models before a device manufactured. However, typical industrial scale devices today involve large number of interactions between their components. Complexity of verifying all interactions becomes almost intractable even in simulation. The infeasible interactions need to be eliminated from verification consideration in order to reduce the complexity of the problem. Also an empirical metric of completeness of the verification of such interactions is needed. This metric should provide measure of quality of verification as well as that of degree of confidence in future correct behavior of the device. Metric should guide stimulus generation for simulation so that all aspects of the device functionality can be covered in verification. Existing coverage metrics focus almost exclusively on verification of individual components. Approach: In this study, interactions between device components modeled as independent processes, were considered. The interactions considered between control flow paths in different processes. Present algorithm analyzed the dependency between the control flow paths. It was also determined set of feasible interactions between the control flow paths and pruned out the infeasible ones. Remaining set of feasible interactions constituted our interaction coverage metric. Our metric handled device designs with an arbitrary number of processes. Results: Number of interactions to be considered in simulation-based verification was significantly reduced by our coverage metric using our proposed algorithms. This limited the complexity and scope of stimulus generation to coverage of only set of feasible interactions. Conclusion: Proposed coverage metric was able to provide realistic measure of degree of verification of components interactions as well as effectively guide the test generation process for device designs consisting of an arbitrary number of components.

Structural topology design optimization using Genetic Algorithms with a bit-array representation

In this paper, a bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is implemented. The importance of structural connectivity in a design is further emphasized by considering the total number of connected objects of each individual explicitly in an equality constraint function. To evaluate the constrained objective function, Deb’s constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the GA. A violation penalty method is proposed to drive the GA search towards the topologies with higher structural performance, less unusable material and fewer separate objects in the design domain. An identical initialization method is also proposed to improve the GA performance in dealing with problems with long narrow design domains. Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly.

CONSTRAINT-HANDLING USING AN EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION TECHNIQUE

In this paper, we introduce the concept of non-dominance (commonly used in multi-objective optimization) as a way to incorporate constraints into the fitness function of a genetic algorithm. Each individual is assigned a rank based on its degree of dominance over the rest of the population. Feasible individuals are always ranked higher than infeasible ones, and the degree of constraint violation determines the rank among infeasible individuals. The proposed technique does not require fine tuning of factors like the traditional penalty function and uses a self-adaptation mechanism that avoids the traditional empirical adjustment of the main genetic operators (i.e., crossover and mutation).