Examining the performance of six heuristic optimisation techniques in different forest planning problems

Two heuristic techniques, the genetic algorithm (GA) and Tabu search (TS), both with an embedded linear programming routine for earthwork allocation, were compared to a manually designed forest road profile. The manually designed road length was 345.7m and its average gradient was 14.1%. The best costs of the profiles designed by GA and TS, without changing the placement of control points, were less than that designed manually. The best cost found by GA was almost the same as the global optimum solution. While TS could not find a better solution than GA, it usually found a good solution in less time. It was not possible to search all alternatives by changing the placement of control points and find the global optimum solution within a reasonable time. However, it can be concluded from the results that both GA and TS found good solutions within a reasonable time. Since it is not possible to manually evaluate many alternatives, road designers should find heuristic techniques helpful for design of the road profile. Moreover, the effect of the number of control points on construction costs was examined. The results indicated that increasing the number of control points reduces the construction costs. However, driving safety and comfort might be decreased.

Although heuristic optimization techniques are increasingly applied in environmental engineering applications, algorithm selection and configuration are often approached in an ad hoc fashion. In this study, the design of a multilayer sorptive barrier system served as a benchmark problem for evaluating several algorithm-tuning procedures, as applied to three global optimization techniques (genetic algorithms, simulated annealing, and particle swarm optimization). Each design problem was configured as a combinatorial optimization in which sorptive materials were selected for inclusion in a landfill liner to minimize the transport of three common organic contaminants. Relative to multilayer sorptive barrier design, study results indicate (i) the binary-coded genetic algorithm is highly efficient and requires minimal tuning, (ii) constraint violations must be carefully integrated to avoid poor algorithm convergence, and (iii) search algorithm performance is strongly influenced by the physical-chemical properties of the organic contaminants of concern. More generally, the results suggest that formal algorithm tuning, which has not been widely applied to environmental engineering optimization, can significantly improve algorithm performance and provide insight into the physical processes that control environmental systems.

Three heuristic search methods, Genetic Algorithm, Simulated Annealing and Tabu Search are implemented to invert Rayleigh wave phase velocity for shallow S-wave velocity profiling in seismic surface wave surveying. Unlike linearized least-squares inversion, they do not require derivative calculation, or an initial model, only the forward modeling calculation. In this study, the performances of the three heuristic techniques are compared with numerical experiments. With common paramerization and search limits, the three algorithms can accurately reconstruct shallow S-wave profiles with and without a velocity reversal from the synthetic data without any specific initial models. The Genetic Algorithms and the Simulated Annealing show fast convergence. However, the Simulated Annealing can find models with the smallest misfits. It is also found that the decrease of the misfits is the most gradual in the Tabu Search.

This review paper provides an in-depth exploration of optimization techniques in Operations Research (OR), highlighting their significance, recent advancements, challenges, and future directions. Operations Research utilizes mathematical and analytical methods to optimize decision-making processes across various industries. The paper begins with an overview of OR, discussing its definition, scope, and historical development. It then delves into the fundamentals of optimization, covering different types of optimization problems and algorithms. Classical optimization techniques such as Linear Programming, Integer Programming, and Nonlinear Programming are discussed, followed by an exploration of heuristic and metaheuristic optimization techniques including Genetic Algorithms, Simulated Annealing, Tabu Search, Particle Swarm Optimization, and Ant Colony Optimization. Recent advances in optimization, including hybrid methods, multi-objective optimization, optimization for big data, and integration with machine learning and artificial intelligence, are analyzed. Furthermore, the paper examines the challenges faced by optimization research and outlines emerging trends and future prospects. By synthesizing current knowledge and identifying areas for future research, this paper aims to contribute to the ongoing development and application of optimization techniques in OR and related fields.

A new branch-and-bound approach for the n/2/flowshop/ αF+ βCmax flowshop scheduling problem

Unmanned air vehicle communication (UAV) systems have recently emerged as a quick, low-cost, and adaptable solution to numerous challenges in the next-generation wireless network. In particular, UAV systems have shown to be very useful in wireless communication applications with sudden traffic demands, network recovery, aerial relays, and edge computing. Meanwhile, non-orthogonal multiple access (NOMA) has been able to maximize the number of served users with the highest traffic capacity for future aerial systems in the literature. However, the study of joint optimization of UAV altitude, user pairing, and power allocation for the problem of capacity maximization requires further investigation. Thus, a capacity optimization problem for the NOMA aerial system is evaluated in this paper, considering the combination of convex and heuristic optimization techniques. The proposed algorithm is evaluated by using multiple heuristic techniques and deployment scenarios. The results prove the efficiency of the proposed NOMA scheme in comparison to the benchmark technique of orthogonal multiple access (OMA). Moreover, a comparative analysis of heuristic techniques for capacity optimization is also presented.

Image fusion is a well-known process in digital image processing. It is extensively used in medical imaging for clinical diagnosis. Clinical image diagnoses like MR-SPECT, MR-PET, MR-CT, and MR: T1-T2 are used day to day basis in medical imaging. Previously many researchers are tried to detect any specific object by using some heuristic optimization technique, but heuristic optimization technique has some fault in its searching process as well as the optimization process. So, it is not worked out. In recent days some researcher has tried meta-heuristic optimization technique in image fusion, but the problem of local optimization restricted there searching flow to find optimum search result. Medical imaging for clinical diagnosis required a high level of precision for detection of a known or unknown object in the human body, but lack of suitable optimization technique, as well as any dedicated hardware, makes the diagnosis process harder than ever. So, here, a version of the modified GW (grey wolf) algorithm with the help of the cuckoo search algorithm has proposed. That not only help the optimization process to find the object in the human body precisely but also allows doctors to take some action in real-time. The optimization algorithm is tasted by using MATLAB R2018b. The proposed design is synthesized using Xilinx Vivado 18.2 synthesis tool and simulated using ModelSim. The outcomes of the synthesis report and simulation of the circuit outshine other metaheuristic optimization approach. This MGWO is performed by using our own designed algorithm.

A New Heuristic Optimization: Search and Rescue Algorithm and Solving the Function Optimization Problems

The N-Queen problem is a classical combinatorial optimization and constraint satisfaction problem that involves placing N queens on an N × N chessboard such that no two queens threaten each other. Due to its exponential search space and increasing computational complexity with larger board sizes, the problem serves as an important benchmark for evaluating the efficiency of heuristic and metaheuristic optimization techniques. This study presents a comprehensive performance analysis of the Simulated Annealing (SA) optimization approach for solving the N-Queen problem across varying board dimensions. The proposed methodology models the N-Queen problem as an optimization task by defining an objective function that minimizes the number of conflicting queen pairs. Simulated Annealing is employed to iteratively explore the solution space, allowing probabilistic acceptance of suboptimal solutions to escape local minima. Key parameters, including initial temperature, cooling schedule, and termination criteria, are systematically tuned to evaluate their impact on convergence speed and solution quality. Experimental results demonstrate that the SA-based approach effectively finds valid solutions for large values of N with high success rates and reduced computational effort compared to exhaustive and deterministic methods. Performance metrics such as solution accuracy, convergence time, number of iterations, and conflict reduction rate are analyzed and discussed. The results indicate that Simulated Annealing offers a robust and scalable solution for the N-Queen problem, particularly for higher-dimensional instances where traditional methods become impractical. Overall, this study highlights the suitability of Simulated Annealing as an efficient metaheuristic for solving complex combinatorial optimization problems and provides insights into its performance characteristics when applied to the N-Queen problem.

Design of prestressed concrete flat slab using modern heuristic optimization techniques

An heuristic search technique for fixed frequency assignment in non-homogeneous demand systems

The benefits of reactive power compensation depend greatly on the placement and size of the compensators. STATCOM is a shunt compensator and one of the important members of the Flexible AC Transmission Systems (FACTS) family that are increasingly being used in long transmission lines in modern power systems. The FACTS devices placement problem is commonly solved using heuristic optimization techniques which are diverse and have been the subject of ongoing enhancements. This chapter, in the first stage, presents a survey of the literature from the last decade that has focused on the various heuristic optimization techniques applied to determine optimal placement and sizing of the STATCOM. In the second stage, the application of the global harmony search (GHS) algorithm as a new meta-heuristic optimization method for determining the optimal location and size of STATCOM in a transmission network is presented. The algorithm is easy to be implemented and capable of finding multiple optimal solutions to the constrained multi-objective problem, providing more flexibility in making decisions about the location of STATCOM. Power system loss reduction, bus voltage profile improvement, voltage stability enhancement and device size are employed as measures of power system performance in the optimization algorithm. The proposed multi-objective GHS algorithm is validated on 57 and 118-bus transmission networks.KeywordsSTATCOMPower lossModal analysisOptimization techniquesHarmony search algorithm

In this paper a nonlinear controller has been designed for Static Synchronous Compensator (STATCOM) in order to improve transient stability of a power system. The nonlinear model of the power system is exactly linearized via state feedback linearization technique. The optimal gain constants of the nonlinear controller are determined applying a special class of non-cooperative dynamic game theory. The performance of this proposed nonlinear STATCOM controller is compared with a fixed structure lead-lag based conventional STATCOM controller. The set of tuning parameters of the conventional controller are optimized through heuristic optimization technique, Genetic Algorithm (GA). The performance of both nonlinear and the conventional controllers are validated with application of a typical transient disturbance to a SMIB test power system. It has been revealed that the proposed nonlinear controller is more effective in the face of contingency in comparison to the GA based controller.

The polychronic economy

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