Crayfish optimization algorithm

The traveling salesman problem (TSP) is one of the most studied problems in computational intelligence and operations research. Since its first formulation, a myriad of works has been published proposing different alternatives for its solution. Additionally, a plethora of advanced formulations have also been proposed by the related practitioners, trying to enhance the applicability of the basic TSP. This chapter is firstly devoted to providing an informed overview on the TSP. For this reason, we first review the recent history of this research area, placing emphasis on milestone studies contributed in recent years. Next, we aim at making a step forward in the field proposing an experimentation hybridizing three different reputed bio-inspired computational metaheuristics (namely, particle swarm optimization, the firefly algorithm, and the bat algorithm) and the novelty search mechanism. For assessing the quality of the implemented methods, 15 different datasets taken from the well-known TSPLIB have been used. We end this chapter by sharing our envisioned status of the field, for which we identify opportunities and challenges which should stimulate research efforts in years to come.

Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics

Unmanned aerial vehicle (UAV) trajectory planning plays an essential role in agricultural production and biological control. To solve the agricultural UAV trajectory planning problem, a multi-mechanism collaborative improved grey wolf optimization algorithm (NAS-GWO) is proposed. In NAS-GWO, the evolutionary boundary constraint processing mechanism is introduced to update the position of the grey wolf individuals that cross the boundary in time to retain the position information of the optimal individuals to the largest degree to enhance the search accuracy of the algorithm. Then, the Gaussian mutation strategy and spiral function are used as perturbation mechanisms to help the algorithm jump out of the local optimum in time to strengthen the exploitation capability of NAS-GWO. Meanwhile, the improved Sigmoid function is used as a nonlinear convergence factor for balancing the exploitation and exploration of the NAS-GWO. By comparing NAS-GWO with ten advanced metaheuristic algorithms on 20 CEC2017 benchmark functions, the experimental results show that the NAS-GWO algorithm has superior merit seeking and robustness. Moreover, the agricultural UAV trajectory planning problem is solved using NAS-GWO. The experimental results show that the NAS-GWO algorithm plans a more viable and stable trajectory path in four different scale missions, while the most important is that it requires less cost. Among them, the algorithm reduces 27.93%, 38.15%, 32.32%, 34.11%, 10.63%, and 13.48% on average in cost function values compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Aquila Optimizer (AO), Differential Evolution (DE), Dung Beetle Optimizer (DBO), and Grey Wolf Optimization algorithm (GWO), thus proving the effectiveness and significance of NAS-GWO in the agricultural UAV trajectory path planning problem. 

Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm

In recent years, the flexible job shop dynamic scheduling problem (FJDSP) has received considerable attention; however, FJDSP with transportation resource constraint is seldom investigated. In this study, FJDSP with transportation resource constraint is considered and an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm (DQNSGA) integrated with reinforcement learning (RL) is proposed. In DQNSGA, an initialization method based on heuristic rules and an insertional greedy decoding approach are designed, and a double-Q Learning with an improved ε-greedy strategy is used to adaptively adjust the key parameters of NSGA-III. An improved elite selection strategy is also applied. Through extensive experiments and practical case studies, this algorithm has been compared with three other well-known algorithms. The results demonstrate that DQNSGA exhibits significant effectiveness and superiority in all tests. The research presented in this paper enables effective adjustments of production plans in response to dynamic events, which is of critical importance for production management in the manufacturing industry. 

A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem

: Metaheuristics are often used to find solutions to real and complex problems. These algorithms can solve optimization problems and provide solutions close to the global optimum in an acceptable and reasonable time. In this paper, we will present a new bio-inspired metaheuristic based on the natural chasing and attacking behaviors of rats in nature, called a Rat swarm optimizer. Which has given good results in solving several continuous optimization problems, and adapted it to solve a discrete, NP-hard, and classical optimization problem that is the traveling salesman problem (TSP) while respecting the natural behavior of rats. To test the efficiency of the adaptation of our proposal, we applied the adapted rat swarm optimization (RSO) algorithm to some reference instances of TSPLIB. The obtained results show the performance of the proposed method in solving the traveling salesman problem (TSP). design design

/pdf/a-novel-discrete-rat-swarm-optimization-drso-algorithm-for-rxgc2r6x.pdf

A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem

Metaheuristics form a family of optimization algorithms for solving combinatorial optimization problems by applying the research procedures to quickly find a good approximation of the best solution. In this paper we proposed a new metaheuristic novel hybrid penguins search optimization algorithm (NPeSOA) which is based on the combination of penguins search optimization algorithm (PeSOA) and harmony search algorithm (HS) to solve the Travelling Salesman Problem. The search for harmony was added to improve the research technique of PeSOA method. The results of this experience are tested by the instances of TSPLib, and compared with the methods of PeSOA and HS to show the efficiency of NPeSOA.

A novel hybrid penguins search optimization algorithm to solve travelling salesman problem

The quadratic assignment problem (QAP) is an NP-hard problem with a wide range of applications in many real-world applications. This study introduces a discrete rat swarm optimizer (DRSO)algorithm for the first time as a solution to the QAP and demonstrates its effectiveness in terms of solution quality and computational efficiency. To address the combinatorial nature of the QAP, a mapping strategy is introduced to convert real values into discrete values, and mathematical operators are redefined to make then suitable for combinatorial problems. Additionally, a solution quality improvement strategy based on local search heuristics such as 2-opt and 3-opt is proposed. Simulations with test instances from the QAPLIB test library validate the effectiveness of the DRSO algorithm, and statistical analysis using the Wilcoxon parametric test confirms its performance. Comparative analysis with other algorithms demonstrates the superior performance of DRSO in terms of solution quality, convergence speed, and deviation from the best-known values, making it a promising approach for solving the QAP.

A novel discrete rat swarm optimization algorithm for the quadratic assignment problem

This paper presents a new hybrid algorithm that combines genetic algorithms (GAs) and the optimizing spotted hyena algorithm (SHOA) to solve the production shop scheduling problem. The proposed GA-SHOA algorithm incorporates genetic operators, such as uniform crossover and mutation, into the SHOA algorithm to improve its performance. We evaluated the algorithm on a set of OR library instances and compared it to other state-of-the-art optimization algorithms, including SSO, SCE-OBL, CLS-BFO and ACGA. The experimental results show that the GA-SHOA algorithm consistently finds optimal or near-optimal solutions for all tested instances, outperforming the other algorithms. Our paper contributes to the field in several ways. First, we propose a hybrid algorithm that effectively combines the exploration and exploitation capabilities of SHO and GA, resulting in a balanced and efficient search process for finding near-optimal solutions for the FSSP. Second, we tailor the SHO and GA methods to the specific requirements of the FSSP, including encoding schemes, objective function evaluation and constraint handling, which ensures that the hybrid algorithm is well suited to address the challenges posed by the FSSP. Third, we perform a comprehensive performance evaluation of the proposed hybrid algorithm, demonstrating its effectiveness in terms of solution quality and computational efficiency. Finally, we provide an in-depth analysis of the behavior of the hybrid algorithm, discussing the roles of the SHO and GA components and their interactions during the search process, which can help understand the factors contributing to the success of the algorithm and provide insight into potential improvements or adaptations to other combinatorial optimization problems.

/pdf/hybrid-genetic-and-spotted-hyena-optimizer-for-flow-shop-fuv99oic.pdf

Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem

The Rat Swarm Optimizer (RSO) algorithm is examined in this paper as a potential remedy for the flow shop issue in manufacturing systems. The flow shop problem involves allocating jobs to different machines or workstations in a certain order to reduce execution time or resource use. The objective function is used by the RSO method to optimize the results after mapping the rat locations to task-processing sequences. The RSO method successfully locates high-quality solutions to the flow shop problem when compared to other metaheuristic algorithms on diverse test situations. This research helps to improve the flexibility, lead times, quality, and efficiency of the production system. The paper introduces the RSO algorithm, creates a mapping strategy, redefines mathematical operators, suggests a method to enhance the quality of solutions, shows how successful the algorithm is through simulations and comparisons, and then uses statistical analysis to confirm the algorithm's performance.

/pdf/optimizing-production-scheduling-with-the-rat-swarm-search-cmup0fg4.pdf

Optimizing production scheduling with the Rat Swarm search algorithm: A novel approach to the flow shop problem for enhanced decision making

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