The Cuckoo Search Algorithm is a recently developed nature inspired meta heuristic algorithm, which is established on the breeding behavior of Cuckoo species. Cuckoo search can be applied on a large variety of optimization problems. The main advantage of this search algorithm is its simplicity and better performance than many other agent or population based meta heuristic algorithms. The algorithm uses only one controlling parameter p, which makes it easier to implement and control. This parameter p, combined with the random walk mutations implemented by Lévy Flights, can control the performance and degree of exploration and exploitation of the algorithm. In this paper we have conducted a few experiments on Cuckoo Search algorithm with Lévy flights to discover the necessary conditions needed for the better performance of the algorithm. For this purpose we have taken different values of the controlling parameter p and observed the performance of the algorithm on benchmark problems, as well as its exploration and exploitation characteristics over different groups of benchmark functions.

This paper conducts an experimental comparison between two recently introduced meta-heuristic algorithms, which are the Differential Evolution (DE) and the Artificial Bee Colony (ABC) algorithm. Both these algorithms are very prominent and significant to represent the broader family of algorithms to which they belong, i.e., the Evolutionary and Swarm Intelligence algorithm families. Both DE and ABC have been successfully employed to numerous and diverse problems from the fields of mathematics, science and engineering. DE is an evolutionary algorithm that computes the vector differences between randomly picked candidate solution vectors and uses these differences to produce new, improved candidate solutions to advance its evolutionary search and optimization process. The ABC is a swarm intelligent algorithm that mimics the candidate solutions as a swarm of bees that forage across a search space for continuously better quality food sources (i.e., candidate solutions). The aim and focus of this paper is to present a side-by-side comparison of these two evolutionary and swarm intelligence algorithms on a common set of continuous benchmark problems to achieve a better understanding of their strengths, weaknesses and characteristics. The experimental results show that ABC is more explorative and can consistently avoid the local optima to locate the neighborhood of the global minimum, while DE is more exploitative to achieve an excellent level of fine tuning, but at the risk of premature convergence because of its lack of explorative characteristics.

The Differential Evolution (DE) is a prominent meta-heuristic algorithm that has been successfully employed to numerous complex and diverse problems from the fields of mathematics, science and engineering. DE belongs to the evolutionary family of algorithms which is based on the Darwinian theory of natural selection and evolution. DE maintains a population of candidate solutions and uses the vector differences between randomly picked candidate solution vectors to produce new, improved solutions to advance its evolutionary optimization process, generation by generation. This paper introduces a novel DE-variantthe DE with Alternating Strategies (DE-AS) and evaluates its performance using a number of benchmark problems on numeric function optimization. DE-AS effectively combines the exploitative and explorative characteristics of five different DE-variants by randomly alternating and executing these DE-variants in a single algorithm. The experimental results indicate that DE-AS can perform better than many other existing DE-variants on most of the benchmark functions, in terms of both final solution quality and convergence speed.

Nature is a great source of inspiration for research tasks. Flower Pollination Algorithm is inspired by the pollination process between different plants. Pollination is a natural process between different flowering plants. By this process plants create their offspring. In the process of pollination in flowers the pollen or gamete of male flower transfers to female flower. This paper has proposed a new variant of Flower Pollination Algorithm for continuous problem on Global Optimization by using Probability Modification.

The Explorative Artificial Bee Colony (EABC) algorithm is a recently introduced swarm intelligence based algorithm that has been successfully tested to optimize only a limited number of multimodal functions. This paper evaluates EABC on a larger number of benchmark functions, including both unimodal and multimodal functions. EABC is an improved variant of the Artificial Bee Colony (ABC) algorithm. A major problem with the basic ABC algorithm is that it is more aligned towards exploitations, rather than explorations, which often leads to premature convergence and fitness stagnation. The improved variant -EABC tries to increase the degree of explorations of ABC by introducing more randomness during its perturbation operations. Besides, EABC customizes the degree of exploitations and explorations at the individual solution level, separately for each candidate solution of the bee population. EABC also introduces a crossover operation that assists the explorative perturbation operation of EABC. This paper extends the experimental studies on EABC by evaluating it on as many as 13 complex, high dimensional benchmark functions, including both unimodal and multimodal, separable and non-separable functions. The results are compared with the basic ABC algorithm. The comparison demonstrates that EABC often performs better optimization than the original ABC algorithm, which indicates the effectiveness of its more explorative operations.