One way to achieve diversification is to re-start the procedure from a new solution once a region has been explored. In this chapter we describe the best known multi-start methods for solving optimization problems. We propose classifying these methods in terms of their use of randomization, memory and degree of rebuild. We also present a computational comparison of these methods on solving the linear ordering problem in terms of solution quality and diversification power. Keywords This is a preview of subscription content, log in to check access. Preview Unable to display preview.
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Published14 Sep Abstract Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm CSA is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
Introduction The solutions to multitudinous domains-whether in engineering design, operational research, industrial process, or economics inevitably have optimization at heart. However, having a solid grasp for such problems turns out to be painstakingly tough and tedious; and thus, gearing towards an efficient and effective algorithm in the light of solving increasingly complex optimization problems in practice is of paramount significance. Extensive and intensive studies in this aspect fruit in numerous optimization techniques, particularly, the bioinspired metaheuristic methods which draw inspiration from the means on how humans and living creatures struggle to survive in a challenging environment, for instance, genetic algorithm GA [ 1 ], particle swarm optimization PSO [ 2 ], differential evolution DE [ 3 ], ant colony optimization [ 4 ], artificial bee colony algorithm [ 5 ], and firefly algorithm [ 6 ], form the hot topics in this area.
Cuckoo search algorithm CSA , another adoption of biomimicry in the optimization technique which reproduces the breeding strategy of the best known brood parasitic bird, the cuckoos, has been proposed by Yang and Deb recently [ 7 , 8 ]. Cuckoos, probably one of the most vicious and cunning species of all bird breeds, clandestinely lay their eggs in the nests of other host birds, sparing themselves the parental responsibilities of raising the young.
In fact, cuckoos practice the art of deception all the time in their reproductive life. They mimic the colour and pattern of the host eggshell in order to disguise their eggs from being detected by the host birds.
To make more space and food for their young chick, cuckoos will steal the host egg while sneaking their own into the nest. However, the relationship between the host species and the cuckoos is often a continuous arms race. The hosts learn to discern the imposters and they either throw out the parasitic eggs or desert the nest; the parasites improve the forgery skill to make their eggs to appear more alike with the host eggs.
The feasibility of applying the CSA to locate the global optimum for the optimization problems has been investigated in the literature. In the pioneering work of Yang and Deb, the CSA has been implemented successfully in optimizing several benchmark functions, and their findings showed that the global search ability of the CSA is more efficient than GA and PSO [ 7 , 8 ].
On the other hand, the CSA has been employed in diverse domains since its inception; including engineering design process [ 9 — 12 ], chaotic system [ 13 ], wireless sensor networks [ 14 , 15 ], structural optimization problem [ 9 , 16 , 17 ], image processing [ 18 , 19 ], milling process [ 20 ], and scheduling problem [ 21 — 23 ]. Undoubtedly, its popularity increases unceasingly in the not-to-distant future.
Nevertheless, in real world situations, obtaining the exact global optimum is impracticable, as the underlying problems are always subjected to various uncertainties and constraints. In this case, instead of finding the actual optimum, the core consideration in selecting an appropriate optimization technique is how much improvement is achievable for a given application at a plausible computational complexity, with an acceptable error.
The main thrust of this paper is therefore geared towards a modified CSA, which integrates an accelerated searching strategy in its computation. The improvement over the CSA is tested and validated through the optimization of several benchmarks. The paper is organized as follows.
In Section 2 , the standard CSA is introduced and its deficiencies are discussed. The modified CSA, specifically, the adaptive cuckoo search algorithm ASCA , is proposed in Section 3 , and the comparative results in evaluating the benchmark optimization functions are presented in Section 4.
Finally, some conclusions are drawn in Section 5. Only the fittest among all the host nests with high quality eggs will be passed on to the next generation; iii the number of host nests in the CSA is fixed beforehand. The host bird spots the intruder egg with a probability.
For such incidents, the host bird will either evict the parasitic egg or abandon the nest totally and seek for a new site to rebuild the nest. Derived from these assumptions, the steps involved in the computation of the standard CSA are presented in Algorithm 1 [ 7 ].
Genetic algorithms for numerical optimization
Adaptive Cuckoo Search Algorithm for Unconstrained Optimization