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Multi-robot path planning (MRPP) in continuous and known environment is studied in this paper via proposing a novel local path planning approach. To plan optimal collision-free paths for multiple robots simultaneously, a novel implementation method suitable for the meta-heuristic algorithms is devised, and an improved artificial bee colony (ABC) algorithm is developed. Three enhancements to the ABC algorithm are made in this context. Firstly, to better lead the search direction, the global best individual is involved in the search equations of employed bee phase and scout bee phase. Meanwhile, to boost exploitation capability, the learning method of teaching-learning based optimization (TLBO) algorithm is incorporated into the onlooker bee phase. The proposed learning-based ABC (ABCL) algorithm is used to determine the subsequent positions for all the robots based on their current coordinates considering the path length, safety and planning efficiency. The experimental studies on benchmark functions show that ABCL is outstanding in solving different types of optimization problems compared against seven effective meta-heuristic algorithms. More importantly, MRPP simulation results prove that ABCL outperforms its competitors in terms of generating optimal collision-free paths and running time. Compared with the original ABC, ABCL improves these two aspects on average for all tasks by 2.1% and 12.6%, respectively. Therefore, with the proposed implementation method, ABCL can be considered as an effective MRPP solution. • A metaheuristic-based approach is proposed for the multi-robot path planning problem. • A learning-based ABC algorithm is developed to solve time-sensitive online problems. • Simulations of different MRPP tasks were completed, validating the designed approach. [ABSTRACT FROM AUTHOR]

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]

• A mathematical model for multi-compartment vehicle routing problem is presented. • Two hybrid metaheuristics are designed. • Simulation results are compared to optimal solutions. • The performance of the proposed algorithms, is measured using the existing approaches. • A real case study is solved using the two proposed approaches. In this paper, a Multi-Compartment Vehicle Routing Problem (MCVRP) is discussed. The main objective of this problem is to minimize the total traveled distance while using a minimum number of trucks. Different product types are loaded into a fleet of homogeneous trucks with an identical capacity of compartments. For this problem, we present a mathematical model in which the total customer demands for each product must be fully delivered by a single truck and not exceed the truck capacity of the compartment. Moreover, the distance traveled by each truck is subject to a set of constraints in our case study. According to the computational results, the optimization approach can yield us the optimal solution only in the small size instances. For large problem instances, two algorithms to solve the MCVRP are proposed: a hybrid artificial bee colony algorithm and a hybrid self-adaptive general variable neighborhood algorithm. The proposed algorithms are tested using a real case study. The computational results are also compared to existing approaches for measuring and evaluating the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract The symbiotic organism search (SOS) is a recently proposed metaheuristic optimization algorithm that simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in an ecosystem. Clustering is a popular data analysis and data mining technique, and k -means clustering is one of the most commonly used methods. However, its effectiveness is highly dependent on the initial solution, and the algorithm may become trapped around local optima. In view of these drawbacks of the k -means method, this paper describes the use of the SOS algorithm to solve clustering problems. Ten standard datasets from the UCI Machine Learning Repository are used to evaluate the effectiveness of SOS against that of optimization algorithms including differential evolution, cuckoo search, flower pollination, particle swarm optimization, artificial bee colony, multi-verse optimizer, and k -means. Experimental results show that the SOS algorithm not only achieves superior accuracy, but also exhibits a higher level of stability. Highlights • The automatic clustering problem gave rise to a new era in cluster analysis with the application of meta-heuristic algorithm. • We develop symbiotic organisms search (SOS) clustering algorithm to deal with automatic clustering problem. • The experimental results are promising and show that the SOS algorithm produced compact and well-separated clusters. [ABSTRACT FROM AUTHOR]

Highlights • A novel hybrid algorithm ABC-TRR is proposed for parameter extraction of PV models. • The ABC-TRR hybridizes Artificial Bee Colony with Trust-Region Reflective method. • The ABC-TRR can exploit the gradient of PV models to greatly improve convergence. • The ABC-TRR is compared with state-of-the-art algorithms using benchmark I-V data. • The new ABC-TRR algorithm achieves the best accuracy, convergence and reliability. Abstract Accurate, efficient and reliable parameter extraction of solar photovoltaic (PV) models from the measured current-voltage (I-V) characteristic curves is important for evaluation, modelling, and diagnosis of the actual operating state of in-situ PV arrays. In recent years, numerical heuristic optimization algorithms based parameter extraction methods have been proposed. However, the efficiency and reliability of these methods are limited due to heuristic or stochastic searching strategies. In this paper, by combining the trust-region reflective (TRR) deterministic algorithm with the artificial bee colony (ABC) metaheuristic algorithm, a new hybrid algorithm ABC-TRR is proposed to improve the parameter extraction of PV models. The ABC-TRR algorithm combines the global exploration capability of the ABC and the local exploitation of the TRR, which achieves a good tradeoff among accuracy, convergence and reliability. The proposed ABC-TRR hybrid algorithm is evaluated and compared with other state-of-the-art algorithms using the standard I-V curves of the benchmark Photowatt-PWP201 PV module and RTC France solar cell as well as the measured I-V curves of a laboratory PV module/string/array. Comprehensive experimental analysis and comparison results demonstrate that the proposed ABC-TRR algorithm achieves the same level of accuracy as the best reported algorithms with the highest overall reliability. More importantly, the ABC-TRR algorithm converges 4.69 times faster than the best-reported algorithms on average. In view of these advantages, the proposed ABC-TRR algorithm is a promising alternative for accurately, efficiently and reliably extracting the parameters of PV models from measured I-V curves. In addition, it was experimentally demonstrated that the parameter extraction result can be used to indicate the partial shading and abnormal degradation conditions. [ABSTRACT FROM AUTHOR]

In this paper a new meta-heuristic algorithm is introduced. This optimization algorithm is inspired by the very popular tool among the technical traders in the stock market called the Fibonacci Indicator. The Fibonacci Indicator uses to predict possible local maximum and minimum prices, and periods in which the price of a stock will experience a significant amount of movement. The proposed Fibonacci Indicator algorithm is validated on several Benchmark functions up to 100 dimensions to have a comparison to algorithms such as DE extensions, PSO extensions, ABC, ABC-PS, CS, MCS and GSA in the ability of convergence and finding the global optimum in different research areas. Finally two engineering design problems are used to show the performance of the algorithm. Application of the proposed Fibonacci Indicator Algorithm in a wide set of benchmark functions has asserted its capability to deal with difficult optimization problems. [ABSTRACT FROM AUTHOR]

Nature-inspired meta-heuristics have gained popularity for solutions to many real-world complex problems, and the artificial bee colony algorithm is one of the most powerful optimisation methods among meta-heuristics. However, inefficient exploitation of onlooker bees prevents the artificial bee colony algorithm from finding the final result accurately and efficiently for complex problems. In this paper, a novel optimisation method is proposed based on the artificial bee colony algorithm. The proposed optimisation method adaptively exploits onlooker bees over generations. In addition, the proposed optimisation method is applied to a stereo-matching problem to minimise the segment-based integer energy function, which is also introduced in this paper. The experimental results show that the proposed optimisation method outperforms state-of-the-art population-based meta-heuristics, such as the genetic algorithm, differential evolution, conventional artificial bee colony, and clonal selection algorithm, for benchmark functions as well as for the stereo-matching problem. [ABSTRACT FROM AUTHOR]

Due to the uncertainty associated with the structure and electrical elements of DC–DC converters and the nonlinear performance of these modules, designing an effective controller is highly complicated and also technically challenging. This paper employs a new control approach based on type-2 fuzzy neural controller (T2FNC) in order to improve the dynamic response of an ultra-lift Luo DC–DC converter under different operational conditions. The proposed controller can rapidly stabilize the output voltage of converter to expected values by tuning the converter switching duty cycle. This controller can tackle the uncertainties associated with the structure of converters, measured control signals and measuring devices. Moreover, a new intelligent method based on firefly algorithm is applied to tune the parameters of T2FNC. In order to demonstrate the effectiveness of the proposed control approach, the proposed controller is compared to PI and fuzzy controllers under different operational conditions. Results validate efficiency of proposed T2FNC. [ABSTRACT FROM AUTHOR]

One of the most studied variant of portfolio optimization problems is with cardinality constraints that transform classical mean–variance model from a convex quadratic programming problem into a mixed integer quadratic programming problem which brings the problem to the class of NP-Complete problems. Therefore, the computational complexity is significantly increased since cardinality constraints have a direct influence on the portfolio size. In order to overcome arising computational difficulties, for solving this problem, researchers have focused on investigating efficient solution algorithms such as metaheuristic algorithms since exact techniques may be inadequate to find an optimal solution in a reasonable time and are computationally ineffective when applied to large-scale problems. In this paper, our purpose is to present an efficient solution approach based on an artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for solving cardinality constrained portfolio optimization problem. Computational results confirm the effectiveness of the solution methodology. [ABSTRACT FROM AUTHOR]

This study proposes a metaheuristic-based clustering ensemble method. It integrates the clustering ensembles algorithm with the metaheuristic-based clustering algorithm. In the clustering ensembles, this study performs an improved generation mechanism and a co-association matrix in the co-occurrence approach. In order to improve the efficiency, a principle component analysis is employed. Furthermore, three metaheuristic-based clustering algorithms are proposed. This paper uses a real-coded genetic algorithm, a particle swarm optimization and an artificial bee colony optimization to combine with clustering ensembles algorithm. The experimental results indicate that the proposed metaheuristic-based clustering ensembles algorithms have better performance than metaheuristic-based clustering without clustering ensembles method. Furthermore, the proposed algorithms are applied to solve a customer segmentation problem. The real problem is come from a mobile application. Among all of the proposed algorithms, the artificial bee colony optimization-based clustering ensembles algorithm outperforms other algorithms. Therefore, the marketing strategy for the real application is made based on the best result. [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]

Artificial bee colony algorithm (ABC) is a recently introduced swarm based meta-heuristic algorithm. ABC mimics the foraging behavior of honey bee swarms. Original ABC algorithm is known to have a poor exploitation performance. To remedy this problem, this paper proposes an adaptive artificial bee colony algorithm (AABC), which employs six different search rules that have been successfully used in the literature. Therefore, the AABC benefits from the use of different search and information sharing techniques within an overall search process. A probabilistic selection is applied to determine the search rule to be used in generating a candidate solution. The probability of selecting a given search rule is further updated according to its prior performance using the roulette wheel technique. Moreover, a memory length is introduced corresponding to the maximum number of moves to reset selection probabilities. Experiments are conducted using well-known benchmark problems with varying dimensionality to compare AABC with other efficient ABC variants. Computational results reveal that the proposed AABC outperforms other novel ABC variants. [ABSTRACT FROM AUTHOR]

Fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruit fly algorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruit fly algorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic firefly algorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate. [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]

As the number of requests in the cloud increases, providers face more problems (e.g., task scheduling and resource management). The exhaustive search for determining the optimal solutions of scheduling problem is usually impractical and hence metaheuristic algorithms have been widely used to evolve solutions for task scheduling. In this paper, we firstly propose a hybrid meta-heuristic algorithm called Hybrid Fuzzy Hitchcock Bird (HFHB) and second, a multi-objective form of HFHB (MOHFHB) is introduced to solve multi-objective problems (e.g., task scheduling). The HFHB algorithm consists of three main enhancements: (a) The first random population of birds is improved, (b) The attack regulator parameter is set with a fuzzy Sugeno-signature, and (c) The dead birds are replaced with new birds. In multi-objective form (MOHFHB), two concepts (i.e., crowding distance and ranking non-dominated solution) are added to determine the optimal Pareto front. In the first part, HFHB and MOHFHB are evaluated as two global optimizers. In the second part, HFHB and MOHFHB are evaluated for a task scheduling problem against Moth Search Algorithm with DE (MSDE), Enhanced Multi-Verse Optimizer (EMVO), Fuzzy Modified Particle Swarm Optimization (FMPSO), and Simulated-annealing-based Bees Algorithm (SBA). The results indicate that HFHB improves makespan by 12.57%, 38.61%, and 35.75%, and resource utilization by 1.14%, 7.30%, and 5.25% compared to FMPSO, MSDE, and EMVO, respectively. [ABSTRACT FROM AUTHOR]

Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCN is affected by the assignation and selection of some network parameters significantly Sparrow search algorithm (SSA) is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of sparrow population. In this paper, a stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN. Firstly, chaotic sparrow search algorithm (CSSA) is designed which mainly utilizes logistic mapping, self-adaptive hyper-parameters, mutation operator to enhance the global optimization capability of SSA; Secondly, as the performance of SCN is related to regularization parameter r and scale factor λ of weights and biases, then CSSA is employed to give better parameters for SCN automatically; Finally, 13 benchmark functions and several datasets are used to evaluate the performance of CSSA and CSSA-SCN respectively. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms. [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]