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Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments. • A Q-learning based multi-strategy integrated ABC algorithm is proposed. • Multiple strategies are introduced to the onlooker bee phase. • Q-learning is utilized to adaptively determine the most suitable strategy. • Novel state and action settings are designed in Q-learning framework. • Proposed algorithm shows superiority in numerical and practical experiments. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired meta-heuristic algorithm, named artificial rabbits optimization (ARO), is proposed and tested comprehensively. The inspiration of the ARO algorithm is the survival strategies of rabbits in nature, including detour foraging and random hiding. The detour foraging strategy enforces a rabbit to eat the grass near other rabbits' nests, which can prevent its nest from being discovered by predators. The random hiding strategy enables a rabbit to randomly choose one burrow from its own burrows for hiding, which can decrease the possibility of being captured by its enemies. Besides, the energy shrink of rabbits will result in the transition from the detour foraging strategy to the random hiding strategy. This study mathematically models such survival strategies to develop a new optimizer. The effectiveness of ARO is tested by comparison with other well-known optimizers by solving a suite of 31 benchmark functions and five engineering problems. The results show that ARO generally outperforms the tested competitors for solving the benchmark functions and engineering problems. ARO is applied to the fault diagnosis of a rolling bearing, in which the back-propagation (BP) network optimized by ARO is developed. The case study results demonstrate the practicability of the ARO optimizer in solving challenging real-world problems. The source code of ARO is publicly available at https://seyedalimirjalili.com/aro and https://ww2.mathworks.cn/matlabcentral/fileexchange/110250-artificial-rabbits-optimization-aro. • A new bio-inspired artificial rabbits optimization (ARO) algorithm is proposed. • ARO is inspired by the survival strategies of rabbits in nature. • The results on 31 well-known test functions show the efficiency of ARO. • ARO yields superior results over its competitors on 5 engineering problems. • ARO is successfully applied in fault diagnosis of rolling bearing. [ABSTRACT FROM AUTHOR]

Land readjustment and redistribution (LR) is an important approach used to realize development plans by converting rural lands to urban land and also providing urban infrastructure. The LR problem, which is a complex challenging real-world problem, is a discrete optimization problem because its structure is similar to TSP (Traveling Salesman Problem) and scheduling problems which are combinatorial optimization problems. Since classical mathematical methods are insufficient for solving NP (Nondeterministic Polynomial) optimization problems due to time limitations, meta-heuristic optimization algorithms are commonly utilized for solving these kinds of problems. In this paper, meta-heuristic algorithms including genetic, particle swarm, differential evolution, artificial bee, and tree seed algorithms are utilized for solving LR problems. The stated meta-heuristic algorithms are used by applying spatial-based crossover and mutation operators depending upon the LR problem on each algorithm. Moreover, a synthetic dataset is used to ensure that the quality of the solution obtained is acceptable to everyone, to prove an optimal solution easily. By utilizing the suggested spatial-based crossover and mutation operators, finding the ideal solution is aimed using the synthetic dataset. In addition, five different modifications on TSA (Tree-Seed Algorithm) are performed and used to solve LR problems. All the modified versions of TSA are carried out only by changing the mechanism of seed reproduction. The novel TSA approaches are respectively named as tcTSA (tournament current), tbTSA (tournament best), pbTSA (personal-best based), t2TSA (double tournament), and elTSA (elitism based). In the experimental studies, the hybrid approach, which includes the crossover and mutation operators, is successfully applied in all of the algorithms under equal conditions for a fair comparison. According to experimental results performed using the dataset, it can be clearly stated that especially t2TSA outperforms all the algorithms in terms of performance and time. [ABSTRACT FROM AUTHOR]

• We propose a novel metaheuristic based on vapor-liquid equilibrium (VLE) process. • We present a new binary programming model for the PBAP modeled via the COP technique. • We perform a large composition between VLE and different already reported methods. Bio-inspired computing is an emerging paradigm which is based on the basics and inspiration of natural phenomena to design new and robust competing techniques. Various nature science areas have motivated the inspiration for the design of new intelligent systems. Chemical engineering is one of them. In Chemistry, the vapor-liquid equilibrium process describes the distribution of chemical species combining two essential phases: vapor phase and liquid phase. Using a binary system of compounds is possible to simulate a search process based on the equilibrium between both phases. In this paper, we propose a new algorithm inspired by this chemical phenomenon for solving a new patient bed assignment problem. This problem consists of assigning patients to beds by considering relevant medical requirements trying to maximize the most covered soft constraints. For that, we take a traditional model, and we transform it by using the constraint optimization paradigm. We test our algorithm on 30 benchmarks taken of Chilean health services. To verify results, we perform statistical comparatives with artificial bee algorithm, ant colony optimization, the bat method, cuckoo search, genetic algorithm, particle swarm optimization, and a random strategy. Computational experiments illustrate that the VLE algorithm properly solved 30 instances, finding all global optimal. In nineteen instances, VLE converged towards the best solution in its median value. In ten instances, the median, the average and the best value, all of them achieved the global optimal. Now, when comparing VLE against other techniques, we can note that VLE is surpassed by the artificial bee colony in two instances only. The rest of the results show VLE as a robust algorithm able to suppress classical, such as genetic algorithm and particle swarm optimization. [ABSTRACT FROM AUTHOR]