Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

Multi-objective swarm intelligence-based (MOSI-based) metaheuristics were proposed to solve multi-objective optimization problems (MOPs) with conflicting objectives. Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. The reference point is determined by the decision maker to guide the search process to a particular region in the true Pareto front. However, HHMO algorithm produces a poor approximation to the Pareto front because lack of information sharing in its population update strategy, equal division of convergence parameter and randomly generated initial population. A two-step enhanced non-dominated sorting HHMO (2SENDSHHMO) algorithm has been proposed to solve this problem. The algorithm includes (i) a population update strategy which improves the movement of hawks in the search space, (ii) a parameter adjusting strategy to control the transition between exploration and exploitation, and (iii) a population generating method in producing the initial candidate solutions. The population update strategy calculates a new position of hawks based on the flush-and-ambush technique of Harris’s hawks, and selects the best hawks based on the non-dominated sorting approach. The adjustment strategy enables the parameter to adaptively changed based on the state of the search space. The initial population is produced by generating quasi-random numbers using Rsequence followed by adapting the partial opposition-based learning concept to improve the diversity of the worst half in the population of hawks. The performance of the 2S-ENDSHHMO has been evaluated using 12 MOPs and three engineering MOPs. The obtained results were compared with the results of eight state-of-the-art multi-objective optimization algorithms. The 2S-ENDSHHMO algorithm was able to generate non-dominated solutions with greater convergence and diversity in solving most MOPs and showed a great ability in jumping out of local optima. This indicates the capability of the algorithm in exploring the search space. The 2S-ENDSHHMO algorithm can be used to improve the search process of other MOSI-based algorithms and can be applied to solve MOPs in applications such as structural design and signal processing

The impact of the International Livestock Research Institute

Providing the first evidence-based global estimates of the many scientific, economic, policy, and capacity development impacts of livestock research in and for developing countries, this volume is an indispensable guide and reference for veterinarians, animal and forage scientists, and anyone working for the equitable and sustainable development of the world's poorer agricultural economies. Livestock is one of the fastest growing agricultural sectors, with most growth occurring in developing countries. For more than four and a half decades one global centre has been mandated to conduct research on leveraging the benefits and mitigating the costs of livestock production in poor countries. This book focuses on the achievements, failures and impacts of the International Livestock Research Institute (ILRI) and its predecessors, the International Livestock Centre for Africa (ILCA) and the International Laboratory for Research on Animal Diseases (ILRAD). The scientific and economic impacts of tropical livestock research detailed in this work reveal valuable lessons for reducing world hunger, poverty and environmental degradation. Describing the impacts of smallholder livestock systems on the global environment, the book also covers animal genetics, production, health and disease control, and livestock-related land management, public policy and economics, all with useful pointers for future livestock-for-development research