The crop Water Footprint (WF) can provide a comprehensive knowledge of the use of water through the demarcation of the amount of the water consumed by different crops. The WF has three components: green (WFg), blue (WFb) and grey (WFgr) water footprints. The WFg refers to the rainwater stored in the root zone soil layer and is mainly utilized for agricultural, horticultural and forestry product...

An important difficulty with multi-objective algorithms to analyze many-objective optimization problems (MaOPs) is the visualization of large dimensional Pareto front. This article has alleviated this issue by utilizing objective reduction approach in order remove non-conflicting objectives from original set. The present work proposed formulation technique social spider (MOSSO) algorithm provid...

Convergence and diversity are two performance requirements that should be paid attention to in evolutionary algorithms. Most multiobjective algorithms (MOEAs) try their best maintain a balance between the aspects, which poses challenge convergence of MOEAs early process. In this paper, many-objective optimization algorithm based on staged coordination selection, consists stages, is proposed sta...

This work assesses the efficacy of evolutionary algorithms (EAs) using an intuitive multidimensional scaling (MDS) visualization evolution a population. We propose use landmark MDS (LMDS) to overcome computational challenges inherent visualizing many-objective and complex problems with MDS. For benchmark we tested, LMDS is akin visually, whilst requiring less than 1% time memory necessary produ...

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics to solve difficult computational optimization problems. There are two main types of hyper-heuristics: selective and generative hyper-heuristics. An online selective hyper-heuristic framework manages a set of low level heuristics and aims to choose the best one at any given time using a performance mea...

In the last decade, there has been a growing interest in multiobjective evolutionary algorithms that use performance indicators to guide the search. A simple and effective one is the S-Metric Selection Evolutionary Multi-Objective Algorithm (SMS-EMOA), which is based on the hypervolume indicator. Even though the maximization of the hypervolume is equivalent to achieving Pareto optimality, its c...

Abstract: In this paper, we propose an extended multi-objective version of single objective optimization algorithm called sperm swarm optimization algorithm. The proposed multi-objective optimization algorithm based on sperm fertilization procedure (MOSFP) operates based on Pareto dominance and a crowding factor, that crowd and filter out the list of the best sperms (global best values). We div...

In evolutionary multi-objective optimization, balancing convergence and diversity remains a challenge and especially for many-objective (three or more objectives) optimization problems (MaOPs). To improve convergence and diversity for MaOPs, we propose a new approach: clustering-ranking evolutionary algorithm (crEA), where the two procedures (clustering and ranking) are implemented sequentially...

Meta-heuristics are algorithms which are applied to solve problems when conventional algorithms can not find good solutions in reasonable time; evolutionary algorithms are perhaps the most well-known examples of meta-heuristics. As there are many possible meta-heuristics, finding the most suitable meta-heuristic for a given problem is not a trivial task. In order to make this choice, one can de...

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse popul...