Discrete cuckoo search for 0-1 knapsack problem

Searching secret key of classical ciphers in the keyspace is a challenging NP-complete problem that can be successfully solved using metaheuristic techniques. This article proposes two metaheuristic techniques: improved genetic algorithm (IGA) and a new discrete cuckoo search (CS) algorithm for solving a classical substitution cipher. The efficiency and effectiveness of the proposed techniques are compared to the existing tabu search (TS) and genetic algorithm (GA) techniques using three criteria: (a) average number of key elements correctly detected, (b) average number of keys examined before determining the required key, and (c) the mean performance time. As per the results obtained, the improved GA is comparatively better than the existing GA for criteria (a) and (c), while the proposed CS strategy is significantly better than rest of the algorithms (i.e., GA, IGA, and TS) for all three criteria. The obtained results indicate that the proposed CS technique can be an efficient and effective option for solving other similar NP-complete combinatorial problems also.

Aiming at the problems of multiple coverage blind areas, poor monitoring quality, and low utilization of sensor network nodes in aviation wireless sensor networks, a discrete cuckoo search (DCS) is proposed, and an objective coverage optimization function is established. Aiming at the fact that the previous cuckoo search is not suitable for discrete values, the binary code conversion formula is used to perform binary code conversion on the jump path of each position update of Levy flight, which makes the algorithm converge faster and avoids the limitations of local search. In the simulation, the discrete cuckoo search was compared with ant colony optimization (ACO) and particle swarm optimization (PSO). Simulation experiment results show that the proposed discrete cuckoo search (DCS) has a great advantage in convergence speed, more targets are detected, and the target coverage in the aerial wireless sensor network is effectively improved.

The binarization of Swarm Intelligence continuous metaheuristics is an area of great interest in operational research. This interest is mainly due to the application of binarized metaheuristics to combinatorial problems. In this article we propose a general binarization algorithm called Percentile Transition Ranking Algorithm (PTRA). PTRA uses the percentile concept as a binarization mechanism. In particular we will apply this mechanism to the Cuckoo Search metaheuristic to solve the set multidimensional Knapsack problem (MKP). We provide necessary experiments to investigate the role of key ingredients of the algorithm. Finally to demonstrate the efficiency of our proposal, we solve Knapsack benchmark instances of the literature. These instances show PTRA competes with the state-of-the-art algorithms.

Max-cut problem is a well-known NP-hard problem, in this paper, a hybrid meta-heuristic algorithm is designed to solve it. In this algorithm, the discrete cuckoo search is employed to find the approximate satisfied solution, while the local optimal solution is used to further improve the performance. To test the validity, three other algorithms are used to compare, simulation results show our modification is effective.

Collaborative beamforming (CB) in wireless sensor networks (WSNs) based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance the energy efficiency of sensor nodes. However, a VNAA cannot be pre-designed like the conventional antenna arrays due to the randomly deployed sensor nodes, thereby causing a high sidelobe level (SLL) which increases the interferences. In this article, we formulate a hybrid discrete and continuous optimization problem (HDCOP) for reducing the maximum SLL. HDCOP requires to solve both the discrete and the continuous problems simultaneously, and we propose both centralized and consensus-based distributed CB strategies for solving HDCOP. For the centralized strategy, we convert HDCOP into two sub-optimization problems, and propose a discrete cuckoo search (CS) algorithm for the node location selection optimization and a continuous CS algorithm to optimize the excitation current weights of the selected nodes. For the distributed strategy, we propose a parallel distributed CS algorithm to solve the discrete and continuous parts of HDCOP simultaneously. Moreover, we propose two operating mechanisms based on these two algorithms. Simulation results verify the effectiveness of the proposed strategies for reducing the maximum SLL of CB in WSNs. Moreover, the proposed CB strategies have better performance in terms of the energy efficiency compared with other approaches such as the cross-entropy optimization-based method.

In this paper, we presented a New Hybrid Binary Particle Swarm Optimization (NHBPSO). This hybridization consists at combining some principles of Particle Swarm Optimization (PSO) and Crossover Operation of the Genetic Algorithm (GA). The proposed algorithm is used to solving the NP-hard combinatorial optimization problem of Multidimensional Knapsack Problem (MKP). In the aim to access the efficiency and performance of our NHBPSO algorithm we have tested it on some benchmarks from OR-Library and we have compared our results with the obtained results by the standard binary Particle Swarm Optimization with penalty function technique (PSO-P) algorithm and the quantum version (QICSA) of the new metaheuristic Cuckoo Search. The experimental results show a good and promise solution quality obtained by the proposed algorithm which outperforms the PSO-P and QICSA algorithms.

A Discrete Cuckoo Search Algorithm for the Cumulative Capacitated Vehicle Routing Problem

Limited edge server resources and uneven distribution of traffic density in vehicular networks result in problems such as unbalanced network load and high task processing latency. To address these issues, we proposed an efficient caching and offloading resource allocation (ECORA) strategy in vehicular social networks. First, to improve the utilization of vehicular idle resources, a collaborative computation and storage resource allocation mechanism was designed using mobile social similarity. Next, with the optimization objective of minimizing the average task processing delay, we studied the combined resource allocation optimization problem and decoupled it into two sub-problems. For the service caching subproblem, we designed a stable matching algorithm by mobile social connections to dynamically update the cache resource allocation scheme for improving the task unloading efficiency. For the task offloading subproblem, a discrete cuckoo search algorithm based on differential evolution was designed to adaptively select the best task offloading scheme, which minimized the average task processing delay. Simulation results revealed that the ECORA strategy outperformed the resource allocation strategy based on particle swarm optimization and genetic algorithm, and reduced the average task processing delay by at least 7.59%. Meanwhile, the ECORA strategy can achieve superior network load balancing.

In this paper, we describe two meta-heuristic algorithms, a discrete cuckoo search algorithm (CS) and a genetic algorithm (GA), for finding of a near optimal solution of an NP-hard combinational sequence-dependent disassembly line-balancing problem. Both algorithms use population-based evolution strategies while they have different ways in exploring a search space. GA is inspired by natural evolution concepts and the search space is explored randomly, while in CS, strategical decisions are made to explore a search space and to update the individuals in a more efficient way. A comparison between two algorithms is performed, in terms of the number of optimal solution occurrences with a number of generations, and the results clearly show that CS algorithm outperforms GA in solving the concerned problem.

Cuckoo search optimization (CSO) algorithm, a recently proposed metaheuristic, has shown promising results in various problem domains. Results from recent studies show that engineering and tuning discrete cuckoo search optimization’ parameters is a daunting task. In this paper, an attempt to enhance the performance of the CSO algorithm in solving discrete combinatorial optimization problems is presented. Performance of the discrete modified CSO algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), hybrid of GA/PSO, and simulated annealing. In addition, a memetic algorithm (MA) that combines discrete modified CSO and tabu search is proposed. Results show that the proposed improvements help in enhancing the performance of the original algorithm. As a test case, the NP-hard problem of buffer minimization in CMOL (CMOS[Formula: see text]+[Formula: see text]nanowire[Formula: see text]+[Formula: see text]MOLecules) circuits is addressed. The performance of the proposed implementation of CSO algorithm is compared with other heuristics.

A Discrete Cuckoo Search Algorithm for the Cumulative Capacitated Vehicle Routing Problem

Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times

This paper presents route planning for a tourist as tourist trip design problem (TTDP) using discrete cuckoo search algorithm (DCS). The DCS algorithm is based on brood parasitic nature of cuckoo bird which is characterized by levy flight. The experiment of proposed DCS is conducted on orienteering problem (OP) benchmark instances and their performance compared with existence data of iterative local search (ILS) and greedy randomized adaptive search procedure (GRASP). Result showed that proposed DCS is required less computational time as compared to ILS and GRASP. It concluded that DCS is efficient method for exploring and exploiting search space in reasonable amount of time.

Correction to: Sleep-wakeup scheduling algorithm for lifespan maximization of directional sensor networks: a discrete cuckoo search optimization algorithm

The manufacturing cell formation problem is considered one of the first issues in the designing of cellular manufacturing systems, that attempts to construct a set of machine cells and their corresponding product families. The aim is to minimise the inter-cell movements of the products while maximising the machine utilisation. Recently developed cuckoo search algorithm is introduced in this paper to solve this kind of problems which is discrete in nature. The proposed method is combined with a local search mechanism in order to intensify the search and improve the quality of the solutions. In order to demonstrate the effectiveness of the proposed algorithm, a set of 35 benchmark problems is used; the results are then compared to different methods collected from the literature. The results demonstrate that the proposed algorithm is a very effective and performs well on all test problems since it can reach 32 out of 35 benchmark problems (91.43%). [Submitted 21 June 2017; Accepted 3 May 2018]