Abstract This paper presents an efficient cooperative multi-populations swarm intelligence algorithm based on the Harris Hawks optimization (HHO) algorithm, named CMPMO-HHO, to solve multi-/many-objective problems. Specifically, this firstly proposes a novel framework with dual elite selection CMPMO/des. With four excellent strategies, namely one-to-one correspondence between objectives and sub...

Abstract Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective may have in single-objective space. For this purpose, we examine inner mechanisms of recently developed sophisticated local search procedure SOMOGSA. method solves problems based first expanding problem an additional ob...

Objectives . A frequently used method for obtaining Pareto-optimal solutions is to minimize a selected quality index under restrictions of the other indices, whose values are thus preset. For scalar objective function, global minimum sought that contains restricted indices as penalty terms. However, landscape such function has steep-ascent areas, which significantly complicate search minimum. T...

Many-objective optimization problems (MaOPs) are challenging in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides objective space into projection plane free dimension(s). The balance between conver...

Multi-modal multi-objective optimization problems (MMMOPs) have multiple subsets within the Pareto-optimal Set, each independently mapping to same Pareto-Front. Prevalent evolutionary algorithms are not purely designed search for solution subsets, whereas, MMMOPs demonstrate degraded performance in objective space. This motivates design of better addressing MMMOPs. The present work identifies c...

Multi-objective optimization is a class of problems with solutions that can be evaluated along two or more incomparable or conflicting objectives. These types of problems differ from standard optimization problems in that the end result is not a single “best solution” but rather a set of alternatives, where for each member of the set, no other solution is completely better (the Pareto set). Mul...

Objective-CP is an optimization system that views an optimization program as the combination of a model. a search, and a solver. Models in Objective-CP follow the modeling style of constraint programming and are concretized into specific solvers. Search procedures are specified in terms of high-level nondeterministic constructs, search combinators, and node selection strategies. Objective-CP su...

We introduce a new multi-objective optimization (MOO) methodology based the splitting technique for rare-event simulation. The method generalizes the elite set selection of the traditional splitting framework, and uses both local and global sampling to sample in the decision space. In addition, an ε-dominance method is employed to maintain good solutions. The algorithm was compared with state-o...

In this paper, application of a multi-objective evolutionary algorithms is explored for solving multi-objective placement, packing, or layout problems. Placement problems are optimization problems concerned with finding an optimal arrangement of multiple items in a large containing region while satisfying prescribed requirements and constraints. This study is applied to the configuration design...