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This paper aims to describe a new hybrid ant colony optimization (ACO) algorithm developed to solve facility layout problems (FLPs) formulated as quadratic assignment problems (QAPs).

A hybrid ACO algorithm which combines max‐min ant system (MMAS) (i.e. a variant of ACO) with genetic algorithm (GA) has been developed. The hybrid algorithm is further improved with the use of a novel minimum pheromone threshold strategy (MPTS).

The hybrid algorithm shows satisfactory results in the experimental evaluation due to the synergy and collaboration between MMAS and GA. The results also show that the use of MPTS helps them to achieve such performance, by promoting search diversification.

The experimental evaluation presented emphasizes more on the search performance or pattern of the hybrid algorithm. Detailed computational work could reveal other strengths of the algorithm.

The developmental work presented in this paper could be used by researchers and practitioners to solve QAPs. Its use may also be expanded to solve other combinatorial optimization and engineering problems.

This paper provides useful insights into the development of a hybrid ACO algorithm that combines MMAS with GA for solving QAPs.

The purpose of this paper is to ensure power efficiency in wireless sensor networks (WSNs) through a new framework-oriented middleware, based on a biologically inspired mechanism that uses an evolutionary multi-objective optimization algorithm. The authors call this middleware framework multi-objective optimization for wireless sensor networks (MONet).

In MONet, the middleware level of each network node autonomously adjusts its routing parameters according to dynamic network conditions and seeks optimal trade-offs among performance objectives for a balance of its global performance. MONet controls the cooperation between agents (network nodes) while varying transmission paths to reduce and distribute power consumption equitably on all the sensor nodes of network. MONet-runtime uses a modified TinyDDS middleware platform.

Simulation results confirm that MONet allows power efficiency to WSN nodes while adapting their sleep periods and self-heal false-positive sensor data.

The framework implementation is lightweight and efficient enough to run on resource-limited nodes such as sensor nodes.

The paper aims to provide an efficient meta-heuristic algorithm to solve the partial set covering problem (PSCP). With rich application scenarios, the PSCP is a fascinating and well-known non-deterministic polynomial (NP)-hard problem whose goal is to cover at least k elements with as few subsets as possible.

In this work, the authors present a novel variant of the ant colony optimization (ACO) algorithm, called Argentine ant system (AAS), to deal with the PSCP. The developed AAS is an integrated system of different populations that use the same pheromone to communicate. Moreover, an effective local search framework with the relaxed configuration checking (RCC) and the volatilization-fixed weight mechanism is proposed to improve the exploitation of the algorithm.

A detailed experimental evaluation of 75 instances reveals that the proposed algorithm outperforms the competitors in terms of the quality of the optimal solutions. Also, the performance of AAS gradually improves with the growing instance size, which shows the potential in handling complex practical scenarios. Finally, the designed components of AAS are experimentally proved to be beneficial to the whole framework. Finally, the key components in AAS have been demonstrated.

At present, there is no heuristic method to solve this problem. The authors present the first implementation of heuristic algorithm for solving PSCP and provide competitive solutions.

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.

Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.

The electricity costs of the bus route can be reduced by applying the optimal charging plans.

This paper produces a viable option for transit agencies to reduce their operation costs.

Grid computing is gaining more significance in the high‐performance computing world. This concept leads to the discovery of solutions for complicated problems regarding the diversity of available resources among different jobs in the grid. However, the major problem is the optimal job scheduling for heterogeneous resources, in which each job needs to be allocated to a proper grid's node with the appropriate resources. An important challenge is to solve optimally the scheduling problem, because the capability and availability of resources vary dynamically and the complexity of scheduling increases with the size of the grid. The purpose of this paper is to present a framework which combines the fuzzy C‐mean (FCM) clustering with an ant colony optimization (ACO) algorithm to improve the scheduling decision when the grid is heterogeneous.

In the proposed model, the FCM algorithm classifies the jobs into appropriate classes, and the ACO algorithm maps the jobs to the appropriate resources. The ACO is characterized by ant‐like mobile agents that cooperate and stochastically explore a network, iteratively building solutions based on their own memory and on the traces (pheromone levels) left by other agents.

The simulation is done by using historical information on jobs in a grid. The experimental results show that the proposed algorithm can allocate jobs more efficiently and more effectively than the traditional algorithms for scheduling policies.

The paper provides a scheduling model based on FCM clustering and ACO algorithm for grid scheduling. The authors compared the performance of the proposed algorithm with the performance of various job‐scheduling algorithms in the grid computing environment. The comparison results show that the proposed algorithm outperforms other algorithms and gives optimal results.

Current industrial scenario is largely dependent on cloud computing paradigms. On-demand services provided by cloud data centre are paid as per use. Hence, it is very important to make use of the allocated resources to the maximum. The resource utilization is highly dependent on the allocation of resources to the incoming request. The allocation of requests is done with respect to the physical machines present in the datacenter. While allocating the tasks to these physical machines, it needs to be allocated in such a way that no physical machine is underutilized or over loaded. To make sure of this, optimal load balancing is very important.

The paper proposes an algorithm which makes use of the fitness functions and duopoly game theory to allocate the tasks to the physical machines which can handle the resource requirement of the incoming tasks. The major focus of the proposed work is to optimize the load balancing in a datacenter. When optimization happens, none of the physical machine is neither overloaded nor under-utilized, hence resulting in efficient utilization of the resources.

The performance of the proposed algorithm is compared with different existing load balancing algorithms such as round-robin load (RR) ant colony optimization (ACO), artificial bee colony (ABC) with respect to the selected parameters response time, virtual machine migrations, host shut down and energy consumption. All the four parameters gave a positive result when the algorithm is simulated.

The contribution of this paper is towards the domain of cloud load balancing. The paper is proposing a novel approach to optimize the cloud load balancing process. The results obtained show that response time, virtual machine migrations, host shut down and energy consumption are reduced in comparison to few of the existing algorithms selected for the study. The proposed algorithm based on the duopoly function and fitness function brings in an optimized performance compared to the four algorithms analysed.

Wireless sensor networks (WSNs) have emerged as one of the most promising technology in our day-to-day life. Limited network lifetime and higher energy consumption are two most critical issues in WSNs. The purpose of this paper is to propose an energy-efficient load balanced cluster-based routing protocol using ant colony optimization (LB-CR-ACO) which ultimately results in enhancement of the network lifetime of WSNs.

The proposed protocol performs optimal clustering based on cluster head selection weighing function which leads to novel cluster head selection. The cluster formation uses various parameters which are remaining energy of the nodes, received signal strength indicator (RSSI), node density and number of load-balanced node connections. Priority weights are also assigned among these metrics. The cluster head with the highest probability will be selected as an optimal cluster head for a particular round. LB-CR-ACO also performs a dynamic selection of optimal cluster head periodically which conserves energy, thereby using network resources in an efficient and balanced manner. ACO is used in steady state phase for multi-hop data transfer.

It has been observed through simulation that LB-CR-ACO protocol exhibits better performance for network lifetime in sparse, medium and dense WSN deployments than its peer protocols.

The proposed paper provides a unique energy-efficient LB-CR-ACO for WSNs. LB-CR-ACO performs novel cluster head selection using optimal clustering and multi-hop routing which utilizes ACO. The proposed work results in achieving higher network lifetime than its peer protocols.

The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years.

The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and computational intelligence.

The paper reviews the basic issues of cluster analysis and discusses the recent advances of clustering algorithms in scalability, robustness, visualization, irregular cluster shape detection, and so on.

The paper presents a comprehensive and systematic survey of cluster analysis and emphasizes its recent efforts in order to meet the challenges caused by the glut of complicated data from a wide variety of communities.