The design of intermodal hub networks is of paramount importance in logistic operations involving multiple transportation modes like trains and trucks. In this work, we consider two non-linear optimization models for ...
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Enhanced index tracking problem (EITP) aims to add sustainable value to portfolio management by emulating the behavior of the benchmark index while limiting the number of assets it holds. There have been numerous ...
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To overcome the mechanical limitations of traditional inertia weight optimization methods, this study draws inspiration from machine learning models and proposes an inertia weight optimization strategy based on the K-...
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To overcome the mechanical limitations of traditional inertia weight optimization methods, this study draws inspiration from machine learning models and proposes an inertia weight optimization strategy based on the K-nearest neighbors (KNN) principle with dynamic adjustment properties. Unlike conventional approaches that determine inertia weight solely based on the number of iterations, the proposed strategy allows inertia weight to more accurately reflect the relative distance between individuals and the target value. Consequently, it transforms the discrete "iteration-weight" mapping (t ! w) into a continuous "distance-weight" mapping (d ! w), thereby enhancing the adaptability and optimization capability of the algorithm. Furthermore, inspired by the entropy weight method, this study introduces an entropy-based weight allocation mechanism in the crossover and mutation process to improve the efficiency of high-quality information inheritance. To validate its effectiveness, the proposed strategy is incorporated into the Seahorse Optimization Algorithm (SHO) and systematically evaluated using 31 benchmark functions from CEC2005 and CEC2021 test suites. Experimental results demonstrate that the improved SHO algorithm, integrating the logistic-KNN inertia weight optimization strategy and the entropy-based crossover-mutation mechanism, exhibits significant advantages in terms of convergence speed, solution accuracy, and algorithm stability. To further investigate the performance of the proposed improvements, this study conducts ablation experiments to analyze each modification separately. The results confirm that each individual strategy significantly enhances the overall performance of the SHO algorithm. Copyright 2025 Zheng
Swarm Intelligence (SI) algorithms, motivated by the group actions of natural systems, offer robust and efficient solutions to complex optimization problems. This review explores several key SI algorithms, including C...
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ISBN:
(数字)9798331519582
ISBN:
(纸本)9798331519599
Swarm Intelligence (SI) algorithms, motivated by the group actions of natural systems, offer robust and efficient solutions to complex optimization problems. This review explores several key SI algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Bat Algorithm (BA), and others. Each algorithm uses ideas from nature, like how ants find food and how bees interact with each other, to effectively solve nonlinear and multimodal problems. Comparative analysis focuses on convergence speed, solution quality, robustness, and computational complexity, highlighting the unique strengths and applicability of each algorithm. SI algorithms can be changed and scaled up or down easily, which makes them useful for dynamic and large-scale optimization tasks. This makes them future-proof solutions that can be used in many areas. Future research directions include new algorithms, hybrid approaches, real-world applications, and integration with machine learning techniques, promising continued advances and broader applicability. The development of SI algorithms is driven by their natural inspiration, simplicity, and potential for innovative applications, underscoring their importance in tackling diverse and complex challenges.
This study analyses and compares the performance of six heuristic algorithms: Genetic Algorithm (GA), Simulated Annealing (SA), Hybrid (SA+GA), Tabu Search (TS), Ant Colony Optimization (ACO), and Particle Swarm Optim...
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ISBN:
(数字)9798331530952
ISBN:
(纸本)9798331530969
This study analyses and compares the performance of six heuristic algorithms: Genetic Algorithm (GA), Simulated Annealing (SA), Hybrid (SA+GA), Tabu Search (TS), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) in achieving the optimal solutions to combinatorial optimization problems represented by 0–1 Knapsack Problem (KP01). The algorithms were analyzed based on evaluation metrics like runtime, iteration time, performance, and complexity. The results indicate large differences in runtime efficiency and iteration speed. For runtime and iteration performance Tabu Search was the fastest with the lowest median runtime, followed by PSO and then GA. On the other hand, the average runtime for ACO is the longest and the highest standard deviation. The average time per iteration ranges from 0.027 ms for SA, to 70.8 ms per iteration for ACO which is very efficient. However, the hybrid method is competitive but provides a longer average time per iteration. Despite the variability, SA, GA, and PSO showed competitive results that can derive their robustness for combinatorial problems. These findings underline several key trade-offs and help to choose an optimal heuristic method that is robust and efficient for real-world decision-making applications.
Shading presents a significant challenge to the efficiency of photovoltaic (PV) panels, often resulting in suboptimal operation and reduced power generation, thereby leading to economic losses. Traditional Maximum Pow...
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In this paper, we propose some heuristic probabilistic polynomial time algorithms with one-sided error for recognition of cubic hypersurfaces the singular loci of which do not contain any linear subspace of sufficient...
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In this paper, we propose some heuristic probabilistic polynomial time algorithms with one-sided error for recognition of cubic hypersurfaces the singular loci of which do not contain any linear subspace of sufficiently large dimension. These algorithms are easy to implement in computer algebra systems. The algorithms are based on checking the condition that the Hessian determinant of a cubic form does not vanish identically or does not determine any cone in the projective space. In turn, the properties of the Hessian can be verified with one-sided-error probabilistic algorithms based on the Schwartz-Zippel lemma.
Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. T...
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Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. This work investigates heuristic algorithms and applications to optimize RIS-aided wireless networks, including greedy algorithms, meta-heuristic algorithms, and matching theory. Moreover, we combine heuristic algorithms with machine learning (ML), and propose three heuristic-aided ML algorithms: heuristic deep reinforcement learning (DRL), heuristic-aided supervised learning, and heuristic hierarchical learning. Finally, a case study shows that heuristic DRL can achieve higher data rates and faster convergence than conventional deep Q-networks (DQNs). This work provides a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML.
Constant weight binary codes are used in a number of applications. Constructions based on mathematical structure are known for many codes. However, heuristic constructions unrelated to any mathematical structure can b...
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Constant weight binary codes are used in a number of applications. Constructions based on mathematical structure are known for many codes. However, heuristic constructions unrelated to any mathematical structure can become of greater importance when the parameters of the code are larger. This paper considers the problem of finding constant weight codes with the maximum number of codewords from a purely algorithmic perspective. A set of heuristic and metaheuristic methods is presented and developed into a variable neighborhood search framework. The proposed method is applied to 383 previously studied cases with lengths between 29 and 63 For these cases it generates 153 new codes, with significantly increased numbers of codewords in comparison with existing constructions. For 10 of these new codes the number of codewords meets a known upper bound, and so these 10 codes are optimal. As well as the ability to generate new best codes, the approach has the advantage that it is a single method capable of addressing many sets of parameters in a uniform way.
This paper addresses a research problem of scheduling parallel, nonidentical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. We were led to this problem...
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This paper addresses a research problem of scheduling parallel, nonidentical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. We were led to this problem through a real-world application involving the scheduling of heat-treatment operations of steel casting. The scheduling of furnaces for heat-treatment of castings is of considerable interest as a large proportion of the total production time is the processing times of these operations. In view of the computational intractability of this type of problem, a few heuristic algorithms have been designed for maximizing the utilization of heat-treatment furnaces of steel casting manufacturing. Extensive computational experiments were carried out to compare the performance of the heuristics with the estimated optimal value (using the Weibull technique) and for relative effectiveness among the heuristics. Further, the computational experiments show that the heuristic algorithms proposed in this paper are capable of obtaining near (statistically estimated) optimal utilization of heat-treatment furnaces and are also capable of solving any large size real-life problems with a relatively low computational effort.
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