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Arithmetic optimization algorithm (AOA) is a meta-heuristic optimization method based on mathematical operators proposed in recent years. Although it has good performance, it can also lead to insufficient local search ability and falling into local optima when solving complex optimization problems. In order to make up for the above shortcomings, the optimization performance of AOA is further improved. This paper proposes a hybrid algorithm based on AOA and particle swarm optimization (PSO) called HAOAPSO. Firstly, a compound opposition-based learning (COBL) strategy is introduced to broaden the scope of finding optimal solutions to help the algorithm better jump out of local optima. Secondly, PSO is combined with AOA that integrates COBL to improve the algorithm's local search ability, so as to improve the overall search efficiency of the algorithm. In addition, experiments are performed on 23 classical benchmark functions with different characteristics and five engineering design optimization problems, and the experimental results of HAOAPSO are compared with those of other well-known optimization algorithms to comprehensively evaluate the performance of the proposed algorithm. The simulation results show that HAOAPSO can provide better solutions in most cases when solving global optimization problems such as engineering, with better convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

The Harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed Harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. Firstly, the pinhole imaging strategy is used to enable the Harris' hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris' hawks more diverse and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy. [ABSTRACT FROM AUTHOR]

A hybrid algorithm based on the sparrow search algorithm (SSA) and whale optimization algorithm (WOA) is proposed to address numerical and engineering optimization problems. The hybrid algorithm has enhanced global search ability through the WOA's improved spiral update mechanism, so that it does not easily fall into the local optimum. Further, using the guard mechanism of SSA introduced by the Levy flight, it has a strong ability to escape from the local optimum. The performance of the improved sparrow search whale optimization algorithm (ISSWOA) was investigated using 23 benchmark functions (classified into standard unimodal, multimodal, and fixed-dimension multimodal benchmark functions) and compared with similar algorithms. The experimental results indicated that ISSWOA was significantly superior to other algorithms on most benchmark functions. To evaluate the performance of ISSWOA in complex engineering problems, seven engineering design problems and a large electrical engineering problem were solved using ISSWOA. Compared with other algorithms, the results showed that ISSWOA had high potential for practical engineering problems. [ABSTRACT FROM AUTHOR]

The marine predators algorithm (MPA) is a metaheuristic algorithm for solving optimization problems. MPA divides the whole optimization process into three phases evenly, and each phase corresponds to a different search agent update strategy. Such a setup makes MPA inflexible when facing different optimization problems, which affects the optimization performance. In this paper, we propose a novel modified MPA hybridizing by Q-learning (QMPA), which applies reinforcement learning to the selection of update strategy, and selects the most appropriate position update strategy for search agents in different iteration stages and states. It can effectively compensate for the deficiency of MPA's adaptive ability when facing different optimization problems. The performance of QMPA is tested on classical benchmark functions, the CEC2014 test suite, and engineering problems. In the classical benchmark functions test, QMPA is compared with MPA in 10, 30, and 50 dimensions. QMPA performs better than MPA for seven of the ten functions when the dimension is 10 and 30. The results of dimension 50 show that QMPA outperforms MPA in 5 functions and is close to it in 4 functions. Then, comparing QMPA with algorithms such as grey wolf optimizer, particle swarm optimization, slime mould algorithm, sine cosine algorithm, reptile search algorithm, and aquila optimizer, the results show that QMPA has the best performance on 22 of the total 30 functions in the CEC2014 test suite. Finally, QMPA is tested on two commonly used real-world engineering problems and gives the most optimal results. In general, the adaptive update strategy proposed in this paper improves the optimization performance of the MPA algorithm in terms of convergence and stability. [ABSTRACT FROM AUTHOR]

Mobile ad hoc networks (MANETs) comprise a collection of independent, compact-sized, and inexpensive sensor nodes, which are commonly used to sense the physical parameters in geographical locations and transmit them to base stations (BSs). Since clustering and routing are considered commonly used energy efficient techniques, several metaheuristic algorithms have been employed to determine optimal cluster heads (CHs) and routes to destinations. However, most metaheuristic techniques have failed to achieve effective clustering and routing solutions in a large search space, and the probability of generating optimal solutions is also considerably reduced. To resolve these issues, this paper presents a new metaheuristic quantum worm swarm optimization-based clustering with a secure routing protocol for MANETs, named QGSOC-SRP. The presented QGSOC-SRP technique follows a two-stage process, namely optimal CH selection and route selection. First, the QGSO algorithm derives a fitness function using four variables, the energy, distance, node degree, and trust factor, for the optimal selection of secure CHs. Second, the SRP using the oppositional gravitational search algorithm (OGSA) is applied for the optimal selection of routes to the BS. The traditional GSA is inspired by the law of gravity and interaction among masses. To improve the effectiveness of the GSA, the OGSA is derived based on the opposition-based learning concept for population initialization and generation jumping. To validate the results regarding the effectiveness of the presented OGSOC-SRP technique, a set of experiments was performed, and the results were determined using distinct measures such as the energy consumption, network lifetime, throughput, and end-to-end delay. [ABSTRACT FROM AUTHOR]

This paper proposes a novel metaheuristic optimizer, named Colony Search Optimization Algorithm (CSOA). The algorithm mimics the social behavior of early humans. Early humans expanded their settlements in search of more livable places to live. In CSOA, the worst solution is used to escape from local optima. And the number of these redundant solutions' updates is reduced to improve the performance of the algorithm. CSOA is tested with 26 mathematical optimization problems and 4 classical engineering optimization problems. The optimization results are compared with those of various optimization algorithms. The experimental results show that the CSOA is able to provide very competitive results on most of the tested problems. Then, a new effective method is provided for solving optimization problems. [ABSTRACT FROM AUTHOR]

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions. [ABSTRACT FROM AUTHOR]

Multilevel thresholding image segmentation is an important technique, which has attracted much attention in recent years. The conventional exhaustive search method for image segmentation is efficient for bilevel thresholding. However, they are time expensive when dealing with multilevel thresholding image segmentation. To better tackle this problem, an improved cuckoo search algorithm (ICS) is proposed to search for the optimal multilevel thresholding in this paper, and Otsu is considered as its objective function. In the ICS, two modifications are used to improve the standard cuckoo search algorithm. First, a parameter adaptation strategy is utilized to improve exploration performance. Second, a dynamic weighted random-walk method is adopted to enhance the local search efficiency. A total of six benchmark test images are used to perform the experiments, and seven state-of-the-art metaheuristic algorithms are introduced to compare with the ICS. A series of measure indexes such as objective function value and standard deviation, PSNR, FSIM, and SSIM as well as the Wilcoxon rank sum and convergence performance are performed in the experiments; the experimental results show that the proposed algorithm is superior to other seven well-known heuristic algorithms. [ABSTRACT FROM AUTHOR]

Feature selection is one of the significant steps in classification tasks. It is a pre-processing step to select a small subset of significant features that can contribute the most to the classification process. Presently, many metaheuristic optimization algorithms were successfully applied for feature selection. The genetic algorithm (GA) as a fundamental optimization tool has been widely used in feature selection tasks. However, GA suffers from the hyperparameter setting, high computational complexity, and the randomness of selection operation. Therefore, we propose a new rival genetic algorithm, as well as a fast version of rival genetic algorithm, to enhance the performance of GA in feature selection. The proposed approaches utilize the competition strategy that combines the new selection and crossover schemes, which aim to improve the global search capability. Moreover, a dynamic mutation rate is proposed to enhance the search behaviour of the algorithm in the mutation process. The proposed approaches are validated on 23 benchmark datasets collected from the UCI machine learning repository and Arizona State University. In comparison with other competitors, proposed approach can provide highly competing results and overtake other algorithms in feature selection. [ABSTRACT FROM AUTHOR]

A novel bio-inspired optimization algorithm called elephant search algorithm (ESA) has been applied to solve NP-hard problems including the classical traveling salesman problem (TS) in this paper. ESA emerges from the hybridization of evolutionary mechanism and dual balancing of exploitation and exploration. The design of ESA is inspired by the behavioral characteristics of elephant herds; hence, the name Elephant Search Algorithm which divides the search agents into two groups representing the dual search patterns. The male elephants are search agents that outreach to different dimensions of search space afar; the female elephants form groups of search agents doing local search at certain close proximities. By computer simulation, ESA is shown to outperform other metaheuristic algorithms over the popular benchmarking optimization functions which are NP-hard in nature. In terms of fitness values in optimization, ESA is ranked after Firefly algorithm showing superior performance over the other ones. The performance of ESA is most stable when compared to all other metaheuristic algorithms. When ESA is applied to the traveling salesman problem, different ratios of gender groups yield different results. Overall, ESA is shown to be capable of providing approximate solutions in TSP. [ABSTRACT FROM AUTHOR]

Local search metaheuristics (LSMs) are efficient methods for solving hard optimization problems in science, engineering, economics and technology. By using LSMs, we could obtain satisfactory resolution (approximate optimum) in a reasonable time. However, it is still very CPU time-consuming when solving large problem instances. As graphic process units (GPUs) have been evolved to support general purpose computing, they are taken as a major accelerator in scientific and industrial computing. In this paper, we present an optimized parallel iterated local search algorithm efficiently accelerated on GPUs and test the algorithm with a typical case study of the Travelling Salesman Problem (TSP) in computational science. We introduce novel methods as follows: first, we present an efficient mapping between a neighborhood and a GPU thread. Second, we use the Roofline model to analyze the performance of existing GPU-based 2-opt kernels. Based on our analysis, we point out the limiting factor of these 2-opt kernels and provide our optimization approaches. Furthermore, we test our algorithm with standard TSP problem instances up to 4461 cities, in which our strategy leads to a speedup factor 279 $$\times $$ over the sequential counterpart. We compare our approach with existing high-performance GPU-based local search algorithms, and the results demonstrate that the proposed algorithm is competitive. [ABSTRACT FROM AUTHOR]

Scheduling dynamically arriving parallel jobs on a grid system is one of the most challenging problems in supercomputer centers. Response time guarantee is one aspect of providing quality of service (QoS) in grids. Jobs are differently charged depending on the response time demanded by the user and the system must provide completion time guarantees. To tackle these challenges, we propose a new type of utility function for defining QoS in user-centric systems. The proposed utility function is a general form of functions in the literature. This function provides customers and system managers with more options to design SLA contracts. Also, its two due dates can make customers more confident and produce more profit for system providers. This paper develops a novel simulated annealing algorithm combined with geometric sampling (GSSA) for scheduling parallel jobs on a grid system. The proposed algorithm is compared with two other methods from the literature using three metrics of total utility, system utilization and the percentage of accepted jobs. The results show that the proposed GSSA algorithm is able to improve the metrics via better use of resources and also through proper acceptance or rejection decisions made on newly arriving jobs. [ABSTRACT FROM AUTHOR]