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In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

The Whale Optimization Algorithm (WOA) is an efficient meta-heuristic algorithm inspired by the feeding behavior of whales. Although it has successfully solved many optimization problems, it still suffers from premature convergence and poor accuracy when solving complex problems. To address these issues, this paper proposes an improved whale optimization algorithm called EIWOA to improve the search efficiency and accuracy. First, we introduce a new global search mechanism and encircling prey strategy in EIWOA, which utilizes differential evolution, and sine-cosine search strategy to improve the global search efficiency and avoid premature convergence. Second, we use a lévy flight-based spiral update position strategy to improve the local search efficiency of the algorithm, thus improving the convergence speed and accuracy. Again, we introduce a balancing factor with fluctuating decay properties into EIWOA to better balance exploration and exploitation. Finally, we introduce a dynamic opposite learning-based whale-fall strategy in EIWOA to equip the algorithm with the ability to jump out of the local optimum. The qualitative analysis of the algorithm shows that the EIWOA algorithm converges quickly, is highly accurate, and has the ability to jump out of the local optimum. In order to validate the performance of the proposed EIWOA algorithm, we evaluated the algorithm on CEC2017 benchmark function and four real world engineering problems. We also conduct a comparative study of the EIWOA algorithm with EWOA, an excellent variant of WOA, as well as some excellent meta-heuristics developed recently. The results of numerical experiments demonstrate the superiority of the proposed EIWOA, which is further confirmed statistically by the results of Friedman's test and Wilcoxon signed rank test. The proposed EIWOA algorithm is significantly better than WOA and other competing algorithms in terms of convergence speed, accuracy and optimization ability in dealing with complex optimization problems. [ABSTRACT FROM AUTHOR]

The Pelican Optimization Algorithm (POA) is a meta-heuristic algorithm that emulates the behavior and hunting strategy of pelicans. This paper presents three modifications that enhance the algorithmic traversal of POA by incorporating ten chaotic mapping factors. These modifications aim to improve the efficiency and accuracy of the traversal process. Firstly, the pelican optimization algorithm with chaotic mapping factors added to population generation but random prey generation (COPOA); secondly, the pelican optimization algorithm with chaotic mapping factors applied to prey generation but random population generation (OCPOA); finally, the pelican optimization algorithm with the same chaotic mapping factor, different sequences generated by different random initial values are mapped to the population and prey generation (CCPOA). The results of simulations on 12 benchmark test functions demonstrate that the algorithm (COPOA) adding the chaotic mapping factor to the population generation alone has higher generalizability, convergence speed and finding accuracy compared with the standard POA algorithm. Among these modifications, the Sine-COPOA method performed the best in ten chaotic mapping factor comparison tests. Furthermore, a comparison is made between the Sine-COPOA and typical intelligent optimization algorithms to assess its competence in optimization. The results show that Sine-COPOA performs better in most of the 12 benchmark test functions. Finally, the engineering design problems (pressure vessel problem and 10-bar truss design) are optimized. The experimental results demonstrate that Sine-COPOA effectively solves both function optimization and engineering optimization problems. [ABSTRACT FROM AUTHOR]

Optimal allocation optimization (OAO) research is an important technology to construct new distribution networks (DNs) with environmental protection and high efficiency. The OAO research in this paper aims to achieve the more ideal operation of DNs by exploring superior allocation schemes including the optimal power factor, access node and capacity of distributed generations (DGs). In order to solve the complex OAO problems, an improved seagull optimization algorithm with elite reserve (ISOAE) is proposed and applied to two different OAO scenarios. In traditional OAO cases with constant loads, the suggested ISOAE finds better DG allocation schemes with less power loss than the basic seagull optimization algorithm (BSOA) and multiple published algorithms. Simulation experiments show that the number, location, capacity and power factor of DGs all have a non-negligible impact on the operation of DNs. In OAO cases with practical seasonal loads, the access of traditional generator (CG), renewable distributed generators represented by photovoltaic (PV) and wind turbine (WT) into the DNs is studied. Multiple experiments on the IEEE 33-bus network demonstrate that CG achieves the relatively best operation status than PV and WT. However, considering resource conservation and environmental protection, renewable PV and WT are more favored by actual power grids. Furthermore, WT operating at the optimal power factor is superior to the PV with uneven radiation intensity distribution in reducing daily active energy loss and increasing node voltage of DNs. In general, the reasonable integration of renewable energy such as WT is of great significance for achieving the two-carbon strategy of power grids. [ABSTRACT FROM AUTHOR]

At present, whale optimization algorithm (WOA) is one of the hot issues in swarm intelligence algorithm. Since it was proposed, people have done a lot of improvement work for WOA algorithm. To address the shortcomings of WOA, an improved WOA combined with the equiangular spiral bubble net predation (named as IWOA) is proposed in this paper. In IWOA, search agent uses the equiangular spiral rather than 9-shaped path to mimics the foraging trajectory of humpback whale. This rule can increase the convergence speed and exploitation ability of the search agent to an extent. Additionally, with the guidance of the sound wave attenuation steering law, search agent in IWOA can switch back and forth between the actively swim (exploitation) and the randomly swim (exploration) to the goal, hence obtain a better tradeoff between the exploitation and exploration. Numerical experiments are conducted on a set of mathematical benchmark cases. The results show that IWOA has a better performance. [ABSTRACT FROM AUTHOR]

The grey wolf optimizer (GWO) is an efficient meta-heuristic algorithm inspired by hunting mechanism of grey wolf. Although it has successfully solved many engineering problems, it still suffers form premature convergence and poor precision in some cases. This paper presents a hybrid grey wolf optimization algorithm (HGWO) with enhanced exploration and exploitation. In order to boost the efficacy of GWO, a new α, β and δ wolf selected method based on random subgroup strategy is proposed and two new global search formulas are designed to enhance the exploration ability. Meanwhile, the concept of greedy wolf is introduced into HGWO to enhance the exploitation ability. Greedy wolf has no hierarchy concept, and only hunting around the most valuable prey to obtain the maximum profit. A nonlinear factor is designed to control the number of greedy wolves to balance the exploration and exploitation. In order to investigate the effectiveness of the proposed HGWO, it was compared with GWO, CGWO (a varint of GWO) and some recently developed algorithms on CEC2017 benchmark functions and four engineering problems. Statistical tests were also employed to investigate the significance of the results. Experimental results and statistical tests demonstrate that the performance of the proposed HGWO is significantly better than that of GWO and other comparison algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective modified seagull optimization algorithm (MOMSOA) is proposed to handle multi-objective environmental economic power dispatch (EED) problems. The algorithm utilizes logistic map to randomize the parameter u, which increases the diversity of the algorithm and speeds up the convergence rate. Besides, the mutation operator is combined to enlarge the range of the search and to avoid falling into the local optimum. The exploration as well as exploitation capabilities for MOSOA are balanced by non-linearizing the original linear parameter A through a cosine dynamic variation strategy. Furthermore, with the assistance of a unique constraint handling approach and non-inferior sorting, MOMSOA can obtain a set of uniform Pareto optimal solution (POS). In order to verify the effectiveness of MOMSOA, experiments were conducted on diverse test systems including IEEE30-Bus, IEEE57-Bus and IEEE118-Bus test systems. During the experimental process, MOSOA and MOPSO/MODE are compared with MOMSOA. Eight trials were conducted, and optimized objective functions include fuel cost, fuel cost with valve point effect, power loss and pollution emission. The results of experiments demonstrate that the proposed MOMSOA can obtain more uniform Pareto fronts (PFs) and optimal best compromise solution (BCS). Finally, two metrics that measure the performance of multi-objective algorithms, SP and HV, are employed to evaluate the results of each algorithm, and the results reveal that solutions obtained by MOMSOA are the most uniform, with the broadest diversity and the best convergence. [ABSTRACT FROM AUTHOR]

This paper demonstrates a novel simple metaheuristic algorithm, the cone search (CS). This name comes from the distinct strategy of CS in searching the suboptimal solution. In the early iteration, its local search space is wide to facilitate exploration. The local search space is reduced linearly during the iteration so that the exploration changes to exploitation gradually. As a swarm intelligence, CS contains several autonomous agents and a collective intelligence called memory. This memory consists of several best solutions. In this work, CS is challenged to find the global optimal of 23 benchmark functions. In the simulation, CS is compared with four metaheuristic algorithms: particle swarm optimization (PSO), marine-predators algorithm (MPA), Komodo mlipir algorithm (KMA), and pelican optimization algorithm (POA). The result shows that CS performs well in solving these 23 functions. Moreover, it can find the global optimal of six fixeddimension multimodal functions: Branin, six hump camel, Hartman 6, Shekel 5, Shekel 7, and Goldstein price. CS beats PSO, MPA, KMA, and POA in solving 23, 22, 21, 22 functions respectively. [ABSTRACT FROM AUTHOR]

School bus service is divided into many kinds of operation modes, such as single school, single load multiple schools and mixed load multiple schools and so on. When it comes to plan bus routes satisfying various constraints and different objectives, there are many applications of school bus routing problem (SBRP). It is generally recognized that middle and large sacle SBRP applications are resolved by heuristic algorithms, because SBRP is a NP-hard problem. This paper proposes a local search-based metaheuristic algorithm framework for SBRP on the basis of analysis of SBRP problem models. The framework, which is implemented by C#, is made up of basic data structures, operation functions, neighborhood operators, initial solution construction algorithm components and heuristic strategies, etc. The neighborhoodcentered metaheuristic algorithms, such as simulated annealing (SA), iterated local search (ILS), variable neighborhood search (VNS), can be designed based on the framework to solve a certain problem type of SBRP. Using this framework, we can solve three operation modes SBRP with homogeneous or heterogeneous bus fleet. Moreover, the proposed metaheuristic framework also provides the development of metaheuristic for the capacitated vehicle routing problem(CVRP) by reducing or modifying the constraints of SBRP. The experimental results of a set of instances show that the metaheuristic algorithm built based on the proposed framework, can be quickly realized and applied to different SBRP applications. Meanwhile, the designed metaheuristic framework is also very effective and extensible. [ABSTRACT FROM AUTHOR]

With the increment in figuring advancements, Cloud computing has permitted the clients to get to assets from anyplace whenever and to pay for the assets based on pay-peruse. Cloud computing allocates the submitted tasks to virtual machine (VM) by dynamic allocation. With the bandwidth and network connectivity getting better, more number of users and organizations are moving to Cloud. All things considered, there are different difficulties that are looked by Cloud service provider. One of the most important challenges is of load balancing. With the number of users gradually increasing, load balancing has become more important. Numerous calculations have been proposed till now for load adjusting, where every one of it centres around the various boundaries. However, these techniques suffer from various issues like - stuck in native optima. None of the calculations has demonstrated to be totally proficient. To deal with the issues identified with existing meta-heuristic procedures, in this paper, a mutation based ant colony optimization load balancing algorithm is proposed. Here we focus on the mutation in Ant Colony Optimization (ACO) algorithm for efficiently balancing the load among various data centers to further develop the presentation boundaries like response time and while improving the overall fitness function. [ABSTRACT FROM AUTHOR]

The Differential Evolutionary algorithm (DE) is a well-known and effective metaheuristic method for continuous optimization problems. However, due to contraction stagnation and early convergence problems, the performance of the DE algorithm can be influenced at the convergence speed and optimization accuracy. Therefore, this paper proposes an improved DE algorithm with the Levy flights mechanism and the Simulated Annealing algorithm (SA), namely DESA-LF algorithm, which effectively uses the advantages of them with generating the better candidate solutions to reduce the convergence speed for making the optimal solution towards the global optimal direction. Finally, we use six typical functions with different complexities and make the comparisons with the DE algorithm, the DE-LF algorithm and other meta-heuristic algorithms to verify the efficient of DESA-LF. From the results and convergence curves, the DESA-LF has the advantages of extending the convergence speed and enhancing the optimization accuracy. Therefore, it demonstrates that convergence speed and optimization accuracy of DESA-LF are highly efficient and superior. [ABSTRACT FROM AUTHOR]

This paper presents a modelling approach for the Transmission Network Expansion Planning (TNEP) problem that considers security constraints through Weighted Transmission Loading Relief (WTLR) nodal indexes. Small scale generation was also included as a complement to candidate solutions to the TNEP. Additionally, this work describes a multi-objective approach for minimizing operating and investment costs while maximizing network security. The latter is modeled through WTLR nodal indexes that allow to measure the severity of overloads under normal operating conditions as well as under contingencies. Two different metaheuristics are implemented and compared: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II). Both techniques enable to find a set of solutions that represent a trade-off between the two objective functions (costs vs. security), over which the system planner is able to make decisions according to a given budget and security target. The results of the Garver’s system and a benchmark IEEE power system support the applicability of the proposed model and the effectiveness of both metaheuristics. [ABSTRACT FROM AUTHOR]