A Cuckoo Search Algorithm for Multimodal Optimization

Many optimization problems in science and engineering are highly nonlinear, and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods, and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms. The standard cuckoo search (CS) is an optimization algorithm based on a single cuckoo species and a single host species. This work extends the standard CS by using the successful features of the cuckoo-host co-evolution with multiple interacting species. The proposed multi-species cuckoo search (MSCS) intends to mimic the co-evolution among multiple cuckoo species that compete for the survival of the fittest. The solution vectors are encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal case studies in practical applications. Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and all optimal solutions are achievable in the tested cases. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm. The comparison has demonstrated the efficiency of the present algorithm. Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases. Therefore, the proposed approach can be a very effective tool for solving nonlinear global optimization problems.

The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. To enhance the accuracy and convergence rate of this algorithm, an improved cuckoo search algorithm is proposed in this paper. Normally, the parameters of the cuckoo search are kept constant. This may lead to decreasing the efficiency of the algorithm. To cope with this issue, a proper strategy for tuning the cuckoo search parameters is presented. Considering several well-known benchmark problems, numerical studies reveal that the proposed algorithm can find better solutions in comparison with the solutions obtained by the cuckoo search. Therefore, it is anticipated that the improved cuckoo search algorithm can successfully be applied to a wide range of optimization problems.

Real-life optimization problems demand robust algorithms that perform efficient search in the environment without trapping in local optimal locations. Such algorithms are equipped with balanced exploration and exploitation capabilities. Cuckoo search (CS) algorithm is also one of these optimization algorithms, which is inspired by nature. Despite effective search strategies such as Lévy flights and solution switching approach, CS suffers from a lack of population diversity when implemented in hard optimization problems. In this paper, enhanced local and global search strategies have been proposed in the CS algorithm. The proposed CS variant uses personal best information in solution generation process, hence called Personal Best Cuckoo Search (pBestCS). Moreover, instead of constant value for switching parameter, pBestCS dynamically updates switching parameter. The prior approach enhances local search ability, whereas the later modification enforces effective global search in the algorithm. The experimental results on test suite with different dimensions validated the efficiency of the proposed modification on optimization problems. Based on statistical and convergence analysis, pBestCS outperformed standard CS algorithm, as well as, particle swarm optimization (PSO) and artificial bee colony (ABC).

Computers & Operations Research, 2011

Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.

SpringerPlus, 2016

Background Cuckoo search (CS) is a population based meta-heuristic algorithm that was developed by Yang et al. (2007). CS (Garg 2015a, d) and other meta-heuristic algorithms such as ant colony optimization (ACO)

Handbook of Research on Soft Computing and Nature-Inspired Algorithms

The chapter at hand seeks to provide a general survey of the Cuckoo Search Algorithm and its most highlighted variants. The Cuckoo Search Algorithm is a relatively recent nature-inspired population-based meta-heuristic algorithm that is based upon the lifestyle, egg laying, and breeding strategy of some species of cuckoos. In this case, the Lévy flight is used to move the cuckoos within the search space of the optimization problem to solve and obtain a suitable balance between diversification and intensification. As discussed in this chapter, the Cuckoo Search Algorithm has been successfully applied to a wide range of heterogeneous optimization problems found in practical applications over the last few years. Some of the reasons of its relevance are the reduced number of parameters to configure and its ease of implementation.

Mathematical Problems in Engineering, 2014

One of the major advantages of stochastic global optimization methods is the lack of the need of the gradient of the objective function. However, in some cases, this gradient is readily available and can be used to improve the numerical performance of stochastic optimization methods specially the quality and precision of global optimal solution. In this study, we proposed a gradientbased modification to the cuckoo search algorithm, which is a nature-inspired swarm-based stochastic global optimization method. We introduced the gradient-based cuckoo search (GBCS) and evaluated its performance vis-à-vis the original algorithm in solving twenty-four benchmark functions. The use of GBCS improved reliability and effectiveness of the algorithm in all but four of the tested benchmark problems. GBCS proved to be a strong candidate for solving difficult optimization problems, for which the gradient of the objective function is readily available.

Cuckoo search algorithm CSA is a recent optimization algorithm of swarm intelligence, which has demonstrated powerful outcomes on many optimization issues. nevertheless, it has some limitations such as stuck in local optima and premature convergence especially When solving complicated problems of optimization. Also, the CSA parameters are static during generations time which lead to stuck in local optima and couldn't find the best solutions. In this paper we proposed an improved standard cuckoo search algorithm based dynamic parameter adjustment mechanism called (CSDPA). The CSDPA presents two equation to update the parameters values of steps size and discovery probability during search process. The experiments are tested on ten conventional benchmark functions. Outcomes demonstrate the new CSDPA approach is outperform of the CSA and another CSA variants.

Swarm and Evolutionary Computation, 2016

Adaptation and hybridization typically improve the performances of original algorithm. This paper proposes a novel hybrid self-adaptive cuckoo search algorithm, which extends the original cuckoo search by adding three features, i.e., a balancing of the exploration search strategies within the cuckoo search algorithm, a self-adaptation of cuckoo search control parameters and a linear population reduction. The algorithm was tested on 30 benchmark functions from the CEC-2014 test suite, giving promising results comparable to the algorithms, like the original differential evolution (DE) and original cuckoo search (CS), some powerful variants of modified cuckoo search (i.e., MOCS, CS-VSF) and self-adaptive differential evolution (i.e., jDE, SaDE), while overcoming the results of a winner of the CEC-2014 competition L-Shade remains a great challenge for the future.

International Journal of Intelligent Systems and Applications, 2014

Today, in computer science, a computational challenge exists in finding a globally optimized solution fro m an enormously large search space. Various meta-heuristic methods can be used for finding the solution in a large search space. These methods can be exp lained as iterative search processes that efficiently perform the exp loration and exp loitation in the solution space. In this context, three such nature inspired meta-heuristic algorith ms namely Krill Herd Algorith m (KH), Firefly Algorith m (FA) and Cuckoo search Algorithm (CS) can be used to find optimal solutions of various mathemat ical optimization problems. In this paper, the proposed algorithms were used to find the optimal solution of fifteen unimodal and mult imodal benchmark test functions commonly used in the field of optimization and then compare their performances on the basis of efficiency, convergence, time and conclude that for both unimodal and mu ltimodal optimization Cuckoo Search Algorith m v ia Lé vy flight has outperformed others and for mu ltimodal optimization Krill Herd algorith m is superior than Firefly algorith m but for un imodal optimization Firefly is superior than Krill Herd algorithm.