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Multimodal optimization problems are challenging problems commonly encountered in diverse domains such as logistics, engineering design, and scientific research. Swarm optimizers are promising candidates for solving these problems. However, ...

As an effective soft computing method, fuzzy cognitive maps (FCMs) have been successfully utilized to process time series prediction problems. However, FCM-based time series prediction models face some challenges including the complicated spatial–...

• Propose a multimodal-optimization-based method to learn high-order FCMs.
• Propose a multimodal artificial bee colony algorithm via nearest-better clustering.
• Experimental results demonstrate the performance of the proposed NABC-...

Multimodal optimization problem (MMOP) refers to the problem having more than one optima or satisfied solution in the decision space. The accuracy and diversity of solutions should be considered when solving MMOPs. In the brain storm optimization ...

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• MMOP refers to the problem of having more than one optima or satisfied solution in the decision space.
• The accuracy and diversity of solutions should be considered when solving MMOPs.
• A KBSOOS algorithm is proposed to enhance the ...

Finding a large set of optima in a multimodal optimization landscape is a challenging task. Classical population-based evolutionary algorithms typically converge only to a single solution. While this can be counteracted by applying ...

Due to the multimodal nature of real-world optimization problems, in recent years, there has been a great interest in multi-modal optimization algorithms. Multimodal optimization problems involve identifying multiple local/global optima. Niching ...

Multimodal optimization problems (MMOPs) refer to problems with multiple optimal solutions in a given search region. Evolutionary algorithms (EAs) are widely used to search for optimal solutions. To address the multimodal problem, we ...

• The NDC method divides populations into niches for species diversification.
• OBL-...

The key of multimodal optimization algorithms is to divide the population into multiple species, and each species searches for a global optimum. This article proposes a new differential evolution based on the Hill-Valley technique for multimodal ...

The design and choice of benchmark suites are ongoing topics of discussion in the multi-objective optimization community. Some suites provide a good understanding of their Pareto sets and fronts, such as the well-known DTLZ and ZDT problems. ...

In the last decades, data has grown exponentially with respect to the number of samples and features. This makes the feature selection (FS) more challenging. In this paper, an optimization method called the multimodal optimization (MMO) technique ...

We advance Mixmash-AIS, a multimodal optimization music mashup creation model for loop recombination at scale. Our motivation is to (1) tackle current scalability limitations in state-of-the-art (brute force) computational mashup models while ...

Test Task Scheduling Problems (TTSPs) are a type of scheduling problems that are very important in many big and complicated test systems in automotive industries where the reliability of the final product is fundamentally dependent on those tests ...

Evolutionary algorithms are widely used to solve multimodal optimization problems. The two main challenges faced while solving MMOPs are locating multiple optimal solutions and improving the accuracy of these solutions. In this paper, ...

Evolutionary algorithms (EAs) that integrate niching techniques are among the most effective methods for multimodal optimization problems. However, most algorithmic contributions are based on empirical performance observations rather ...

Single-objective continuous optimization can be challenging, especially when dealing with multimodal problems. This work sheds light on the effects that multi-objective optimization may have in the single-objective space. For this purpose, we ...

Truss design is a well-known structural optimization problem that has important practical applications in various fields. Truss design problems are typically multimodal by nature, meaning that it offers multiple optimal solutions concerning the ...

The loser-out tournament-based firework algorithm (LoTFWA) is a new baseline among firework algorithm (FWA) variants due to its outstanding performance in multimodal optimization problems. LoTFWA successfully achieves information-interaction among ...

In this paper, an adaptive neighborhood mutation based memetic differential evolution is proposed for multimodal optimization. In the proposed method, an adaptive neighborhood mutation (ANM) strategy is devised to allow the individuals ...

Multimodal optimization is a very difficult task to search for all optimal solutions at once in optimization problems with multiple optimal solutions. Speciation using a proximity graph has been proposed to solve multimodal optimization problems. ...$$$$$$$$$$$$

This paper proposes a grid-guided particle swarm optimizer for solving multimodal multi-objective optimization problems that may have multiple disjoint Pareto sets corresponding to the same Pareto front. The concept of grid in the ...

• A grid-based technique is employed to increase the population diversity and improve the search efficiency.

Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)...

• Multimodality in continuous multi-objective optimization (MOO) is defined very differently.