Engineering efficiency unleashed: minimum initial marking estimation in labeled Petri nets through a cutting-edge hybrid evolutionary heuristic

For the minimum initial marking (MIM) problem is one of minimum resource allocation problems, it is significant to study the MIM problem for a class of hybrid timed Petri nets, called a hybrid timed event graph (HTEG). An HTEG has additional continuous places and continuous transitions than a timed event graph (TEG). By the construction of a new dioid endowed with the pointwise minimum as addition and the composition of functions as multiplication, a linear min-plus algebraic model of HTEG was derived. Based on the min-plus algebra and its properties, the MIM problem for HTEG was studied in the text.

Given a labeled Petri net with silent (unobservable) transitions, we are interested in performing initial marking estimation in a probabilistic setting. We assume a known finite set of initial markings, each with some a priori probability, and our goal is to obtain the conditional probabilities of initial markings of the Petri net, conditioned on an observed sequence of labels. Under a Markovian assumption on the probabilistic model, we develop a recursive algorithm that allows us to efficiently determine the conditional probabilities for each possible initial marking (conditioned on the sequence of observations seen so far). We illustrate the proposed methodology via an example and discuss potential applications in the context of initial state opacity for security applications.

Multistage interconnection networks (MINs) are popular in switching and communication applications and have been used in telecommunication and parallel computing systems for many years. Crosstalk a major problem introduced by an optical MIN, is caused by coupling two signals within a switching element. We focus on an efficient solution to avoiding crosstalk by routing traffic through an N X N optical network to avoid coupling two signals within each switching element using wavelength-division multiplexing (WDM) and a time-division approach. Under the constraint of avoiding crosstalk, the interest is on realizing a permutation that uses the minimum number of passes for routing. This routing problem is an NP-hard problem. Many heuristic algorithms are already designed by researchers to perform this routing such as a sequential algorithm, a degree-descending algorithm, etc. The genetic algorithm is used successfully to improve the performance over the heuristic algorithms. The drawback of the genetic algorithm is its long running times. We use the simulated annealing algorithm to improve the performance of solving the problem and optimizing the result. In addition, a wavelength lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using heuristic, genetic, and simulated annealing algorithms. Many cases are tested and the results are compared to the results of other algorithms to show the advantages of simulated annealing algorithm.

The existence of multiple decision-makers and goals, spatial and non-linear forest management objectives and the combinatorial nature of forest planning problems are reasons that support the use of heuristic optimisation algorithms in forest planning instead of the more traditional LP methods. A heuristic is a search algorithm that does not necessarily find the global optimum but it can produce relatively good solutions within reasonable time. The performance of different heuristics may vary depending on the complexity of the planning problem. This study tested six heuristic optimisation techniques in five different, increasingly difficult planning problems. The heuristics were evaluated with respect to the objective function value that the techniques were able to find, and the time they consumed in the optimisation process. The tested optimisation techniques were 1) random ascent (RA), 2) Hero sequential ascent technique (Hero), 3) simulated annealing (SA), 4) a hybrid of SA and Hero (SA+Hero), 5) tabu search (TS) and 6) genetic algorithm (GA). The results, calculated as averages of 100 repeated optimisations, were very similar for all heuristics with respect to the objective function value but the time consumption of the heuristics varied considerably. During the time the slowest techniques (SA or GA) required for convergence, the optimisation could have been repeated about 200 times with the fastest technique (Hero). The SA+Hero and SA techniques found the best solutions for non-spatial planning problems, while GA was the best in the most difficult problems. The results suggest that, especially in spatial planning problems, it is a benefit if the method performs more complicated moves than selecting one of the neighbouring solutions. It may also be beneficial to combine two or more heuristic techniques.

Problem statement: Spam is an irrelevant or inappropriate message sent on the internet to a large number of newsgroups or users. A spam word is a list of well-known words that often appear in spam mails. Bloom Filter (BF) is used for identification of spam word. Approach: BF is a simple but powerful data structure that can check membership to a static set. The trade-off to use BF is a certain configurable risk of false positives. The odds of a false positive can be made very low if the hash bitmap is sufficiently large. Bin Bloom Filter (BBF) has number of BFs which assign group of words into bins with different false positive rates based on weight of the spam words. Genetic Algorithm (GA) was employed to minimize the total membership invalidation cost of BBF. GA had premature convergence problem. Simulated Annealing (SA) was incorporated with GA to prevent the premature convergence effectively. Results: The experimental results of total membership invalidation cost are analyzed for various sizes of bins. The results showed that the combined GA-SA model outperforms SA and GA model. Conclusion: GA has premature convergence due to its genetic operators that are not able to generate offsprings which are superior to the parents. So more number of similar chromosomes presented on the population. When GA is incorporated with SA new genes were introduced which causes diversity in the population and prevents premature convergence. The combined GA-SA outperforms GA and SA.

Achieving high stable Power Transfer Efficiency (PTE) in Wireless Power Transfer (WPT) systems remains challenging due to the nonlinear, multimodal nature of the optimization space. Conventional algorithms such as Genetic Algorithms (GA), Differential Evolution (DE), and Simulated Annealing (SA) often face premature convergence, sensitivity to parameter settings, and inconsistent performance across runs. To overcome these issues, this study introduces the Evolutionary-Swarm Hybrid Algorithm (ESHA), which integrates DE for directional exploration, GA crossover for population diversity, SA for adaptive convergence, and Lévy Flights for stochastic global search. ESHA was implemented on a WPT system with a fixed 20 cm transmission distance and compared with GA, DE, and SA using three performance indicators: PTE, convergence speed, and computational efficiency. Results show that ESHA achieved a maximum PTE of 97.18%, surpassing GA (96.81%), DE (96.65%), and SA (96.19%), while maintaining zero variance across independent runs. It converged in an average of 31.2 iterations, slightly faster than GA (33.15) and SA (32.1), and comparable to DE (31.3). Execution time was 0.4738 s, close to GA (0.4654 s) and only marginally higher than DE (0.4262 s) and SA (0.4329 s). Statistical validation confirmed significant improvements in PTE (p < 0.05).

Scheduling programs with repetitive projects: A comparison of a simulated annealing, a genetic and a pair-wise swap algorithm

A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

Aiming at the disadvantage of premature convergence of basic genetic algorithm, an adaptive simulated annealing genetic tabu search algorithm is proposed. This algorithm fully combines the global convergence and adaptability of simulated annealing algorithm and the strong climbing ability and high efficiency of tabu search strategy. It has strong convergence and adaptability. The simulation results of the adaptive simulated annealing genetic tabu search algorithm are given and compared with the basic genetic algorithm and simulated annealing algorithm. The simulation results show that the algorithm has better convergence and optimization performance, and can better solve the combinatorial optimization problem.

My research in optimization at the Department of Systems and Computer Engineering at Carleton University has brought me into contact with researchers in the Department of Electronics, our sister department in the general field of Electrical and Computer Engineering (ECE), which has a major research thrust in computer-aided design (CAD) of very large scale integrated (VLSI) circuits. I have come to realize that the design of VLSI systems consists in large part of solving incredibly massive mixed-integer nonlinear optimization problems, together with enormous circuit simulations. This is impossible without the use of CAD techniques. Over time, ECE-CAD researchers have both borrowed useful standard techniques from operations research (OR) and have invented their own, often to deal with the sheer scale and complexity of the design problems they face. Interaction between the OR and ECE-CAD communities would seem to be a natural development. Coincidentally, the INFORMS Journal on Computing had an area entitled “High Performance Computation” that covered (i) the solution of OR problems using new computing technologies, (ii) the application of OR techniques in the design and use of highperformance computing and communication systems, and (iii) solution methods for ultra-large-scale OR applications. The viability of this area was debated during 2003 as some of its aspects migrated to other areas (e.g., the new “Telecommunications and Electronic Commerce” area), and the number of submissions declined. The area was eventually closed, although I argued that the overlap between OR and ECE-CAD, essentially covering items (ii) and (iii), was a very active research area. This Special Cluster of papers was conceived as a way to test that argument. I was recruited at the same time to prepare a tutorial on ECE-CAD for the 2004 INFORMS Annual Meeting (see John W. Chinneck, Michel Nakhla, and Q. J. Zhang 2004. Computer-aided design for electrical and computer engineering. H. J. Greenberg, ed. Tutorials on Emerging Methodologies and Applications in Operations Research. Springer, New York, 6-1 to 6-44). The preparation of the article reinforced my observation about the research overlap between OR and ECE-CAD. A search of the electrical engineering literature for 2000 through early 2004 turned up 46,725 papers mentioning “simulate” or “simulation” as keywords in the abstract, 14,216 papers mentioning “optimization” or “optimize,” and relatively smaller numbers for specific techniques such as “neural network” (6,251), “genetic algorithm” (2,603), “linear programming” (576), “simulated annealing” (448), and “branch and bound” (199). One surprise was the relatively small number of papers using the generic keywords “mathematical programming” (68) or “operations research” (37). The general conclusion of the tutorial article is that the OR and the ECE-CAD communities have much to offer each other. This is certainly the case for the papers gathered in this Special Cluster. We see known OR techniques adapted for use in ECECAD. In “Integer Linear Programming Models for Global Routing,” Behjat et al. apply integer linear programming in a heuristic to solve enormous NPhard connection routing problems for VLSI circuits. In “Task Scheduling in a Finite-Resource, Reconfigurable Hardware/Software Codesign Environment,” Loo and Wells use simulated annealing, genetic algorithms, and random search techniques to solve scheduling problems in hardware-software co-design. We also see new techniques specifically developed by the ECE-CAD community to deal with problems of extreme scale. In “A Projection-Based Reduction Approach to Computing Sensitivity of Steady-State Response of Nonlinear Circuits,” Pai et al. develop methods for sensitivity analysis in extremely large nonlinear programs, especially those in which the objective function is very costly to evaluate. The methods have special relevance for simulation-based

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

Supervised Neural Network (NN) learning is a process in which input patterns and known targets are presented to a NN while it learns to recognize (classify, map, fit, etc.) them as desired. The learning is mathematically defined as an optimisation problem, i.e., an error function representing the differences between the desired and actual output, is being minimized (Bishop, 1995; Haykin, 1999). Because the most popular supervised learning techniques are gradient based (Backpropagation BP), they suffer from the so-called Local Minima Problem (Bishop, 1995). This has motivated the employment of Global Optimisation (GO) methods for the supervised NN learning. Stochastic and heuristic GO approaches including Evolutionary Algorithms (EA) demonstrated promising performance over the last decades (Smagt, 1994; Sexton et al., 1998; Jordanov & Georgieva, 2007; etc.). EA appeared more powerful than BP and its modifications (Sexton et al., 1998; Alba & Chicone 2004), but hybrid methods that combine the advantages of one or more GO techniques and local searches were proven to be even better (Yao, 1999; Rocha et al., 2003; Alba & Chicano, 2004; Ludemir et al., 2006). Hybrid methods were promoted over local searches and simple population based techniques in Alba & Chicone (2004). The authors compared five methods: two BP implementations (gradient descent and Levenberg-Marquardt), Genetic Algorithms (GA), and two hybrid methods, combining GA with different local methods. The methods were used for NN learning applied to problems arising in medicine. Ludemir et al. (2006) optimized simultaneously NN weights and topology with a hybrid method combining Simulated Annealing (SA), Tabu Search (TS) and BP. A set of new solutions was generated on each iteration by TS rules, but the best solution was only accepted according to the probability distribution as in conventional SA. Meanwhile, the topology of the NN was also optimized and the best solution was kept. Finally, BP was used to train the best NN topology found in the previous stages. The new methodology compared favorably with SA and TS on four classification and one prediction problems. Plaginakos et al. (2001) performed several experiments to evaluate various training methods – six Differential Evolution (DE) implementations (with different mutation operators), BP, BPD (BP with deflection), SA, hybridization of BP and SA (BPSA), and GA. They reported O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

In this study, simulated annealing (SA) and genetic algorithm (GA) soft computing techniques are integrated to search for a set of optimal cutting conditions value that leads to the minimum value of machining performance. Twointegration systems are proposed; integrated SA–GA-type1 and integrated SA–GA-type2. The considered machining performance is surface roughness (R a) in end milling. The results of this study showed that both of the proposed integration systems managed to estimate the optimal cutting conditions, leading to the minimum value ofmachining performance when compared to the result of real experimental data. The proposed integration systems have also reduced the number of iteration in searching for the optimal solution compared to the conventional GA and conventional SA, respectively. In other words, the time for searching the optimal solution can be made faster by using the integrated SA–GA.

Rule Based Scheduling Algorithm have been widely used in the cloud computing as it is simple and easy to implement the Scheduling criteria in terms of energy efficiency and less delay, In this paper, we propose Improved Hyper Heuristic Scheduling which is used to find the candidate solution (low level heuristic) form Scheduling Solutions (heuristics algorithms) from the simulated annealing and genetic algorithm in dynamic large scale Cloud Computing system with diversity operator as sequence dependent and sequence independent scheduling. Specifically, Resources and workloads characterised using the simulated annealing and improved genetic algorithm with n point crossover. Hyper heuristic algorithm is used select best possible solution to the dynamic workload to candidate solutions. The Simulation results on cloudsim proves that proposed system outperforms existing state of approaches in terms of reduced make span and flow time for the task scheduling and resource management

2차원 저장소 적재는 NP-hard 문제로서 그 문제의 정확한 해를 구하는 것이 어려운 것으로 알려져 있으며, 이의 더 좋은 해를 얻기 위해 유전자(genetic) 알고리즘, 시뮬레이티드 어닐링(simulated annealing), 타부서치(tabu search)등과 같은 근사적 접근법이 제안되어 왔다. 하지만 분지한계(branch-and-bound)나 타부서치 기법들을 이용한 기존의 대표적인 근사 알고리즘들은 휴리스틱 알고리즘의 해에 기반을 둠으로 효율성이 낮고 반복수행에 의한 계산시간이 길다. 따라서 본 논문에서는 이러한 근사 알고리즘의 복잡성을 간소화하고, 알고리즘의 효율성을 높이기 위해 적재가능성을 판단하는 적합성 함수(fitness function)를 정의하고 이를 이용하여 어떤 특정 개체의 적재영역을 판단하는데 영향을 주는 적재영역의 수를 계산한다. 또한, 이들을 이용한 새로운 휴리스틱 알고리즘을 제안하였다. 끝으로 기존의 휴리스틱 또는 메타휴리스틱 기법과의 비교실험을 통해 기존의 휴리스틱 알고리즘인 FFF와 FBS에 비해 97%의 결과가 같거나 우수하였으며, 타부서치 알고리즘에 비해 86%의 결과가 같거나 우수한 것으로 나타났다. The two-dimensional bin packing problem(2D-BPP) has been known to be NP-hard, and it is difficult to solve the problem exactly. Many approximation methods, such as genetic algorithm, simulated annealing and tabu search etc, have been also proposed to gain better solutions. However, the existing approximation algorithms, such as branch-and-bound and tabu search, have shown the low efficiency and the long execution time due to a large of iterations. To solve these problems, we first define the fitness function to simplify and increase the utility of algorithm. The function decides whether an item is packed into a given area, and as an important information for a packing strategy, the number of subarea that can accommodate a given item is obtained from the variant of the fitness function. Then we present a heuristic algorithm BF for 2D bin packing, constructed by the fitness function and subarea. Finally, the effectiveness of the proposed algorithm will be expressed by the comparison experiments with the heuristic and the metaheuristic of the literatures. As comparing with existing heuristic algorithms and metaheuristic algorithms, it has been found that the packing rate of algorithm BP is the same as 97% as existing heuristic algorithms, FFF and FBS, or better than them. Also, it has been shown the same as 86% as tabu search algorithm or better.